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At least 685 records · Page 38Linked to original sources
PubMed2000

[ROC curve analysis of factors predictive of no response to interferon treatment in patients with chronic hepatitis C, genotype 1]

The objectives of this study included: 1) to identify pretreatment variables predictive of absence of response in 107 patients with chronic hepatitis C, genotype 1, treated with interferon-a (IFN-a) at a dose of 3 MU three times weekly for 3-12 months and classified into two groups: group A, nonresponders vs. patients with a complete response, and group B, nonresponding and relapsing patients vs. patients with a sustained response; and 2) to establish a prognostic index using ROC curve analysis. The rate of sustained response was 6.5% at the 24-month follow-up. The pretreatment characteristics with predictive value using ROC curves were as follows: in group A, age, GGT, serum ferritin, viral load, and grade and stage of the histological lesion; and in group B, known duration of infection, GPT, GGT, serum ferritin, viral load, and grade and stage of the histological lesion. In both group A and group B the positive predictive value (the probability of predicting an absence of response when the variable is present) was greater than the negative predictive value (mean: 84.3% vs. 41.1%, 99% vs. 16.5%, respectively). In group A, based on the prognostic index, the positive predictive value when three variables were present was 96% and the sensitivity was 63.5%, with the test being unequivocal in 6.5%, whereas when four or five variables were present, the positive predictive value was 97% and 100% and the sensitivity was 40.5% and 18%, respectively. In group B, the positive predictive value when two variables were present was 100% and the sensitivity was 87%, whereas when three, four, five and six variables were present the sensitivity was between 73% and 28%. In group A, age, GGT and ferritin were the predictive variables independently associated with an absence of response, with a relative risk of 6.5, 4.8 and 3.1, respectively, whereas in group B we did not find variables independently associated with an absence of response. It was concluded that in patients with genotype 1, it is possible to predict the absence of response to IFN therapy with a high degree of reliability.

Journal Article↗
PubMed2000

Prediction of two- and three-amino-acid sequences of Citrobacter Freundii beta-lactamase from its amino acid composition.

The repeated amino-acid sequences in Citrobacter Freundii beta-lactamase may be indispensable for its function, because such repetitions cannot be simply attributed to a chance. In order to fully explore the functional units in Citrobacter Freundii beta-lactamase, it may need to analyse all the amino acid pairs, triplets, etc. along Citrobacter Freundii beta-lactamase from one terminal to the other terminal, to count their frequencies and calculate their probabilities. The amino-acid sequence of Citrobacter Freundii beta-lactamase was counted according to two-, three- and four-amino-acid sequences. The counted frequency and probability were compared with the predicted frequency and probability. The amino acid sequences, which appear in Citrobacter Freundii beta-lactamase and can be predicted from its amino acid composition according to a purely random mechanism, should not be deliberately evolved and conserved. By contrast, the amino acid sequences, which appear in Citrobacter Freundii beta-lactamase but cannot be predicted from its amino acid composition according to a purely random mechanism, should be deliberately evolved and conversed. Accordingly 99 (26.053%) and 33 (8.684%) of 380 two-amino-acid sequences can be predicted by the frequency and probability according to a purely random mechanism. Some kinds of amino acid sequences, which absent in Citrobacter Freundii beta-lactamase and can be predicted from its amino acid composition according to a purely random mechanism, should not be deliberately excluded from Citrobacter Freundii beta-lactamase. By contrast, some kinds of amino acid sequences, which absent in Citrobacter Freundii beta-lactamase and cannot be predicted from its amino acid composition according to a purely random mechanism, should be deliberately excluded from Citrobacter Freundii beta-lactamase. Accordingly 89 (48.370%) and 41 (22.283%) of 184 kinds of absent two-amino-acid sequences can be predicted by the frequency and probability according to a purely random mechanism, and 7236 (99.848%) of 7247 kinds of absent three-amino-acid sequences can be predicted by the frequency according to a purely random mechanism. The amino acids, whose probabilities in following certain preceding amino acids can be predicted from Citrobacter Freundii beta-lactamase amino acid composition according to a purely random mechanism, should not be deliberately evolved and conversed, accordingly 2 (0.526%) of 380 counted first order Markov transition probabilities for the second amino acid in two-amino-acid sequences match the predicted conditional probabilities.

Citrobacter↗
PubMed1975

The prediction of maximal oxygen uptake before and after physical training in children.

Maximal oxygen consumption (V O2 max) expressed in ml/kg/min and predicted V O2 max were determined before and after 8 weeks of training in 24 boys 10-12 years. Training involved 13 of them while 11 were controls. Predicted V O2 max was based on submaximal cycling heart rate according to the Astrand-Rhyming procedure. Pre-training, V O2 max was underpredicted by 12 per cent. This resulted mainly from an apparently low cycling efficiency in these subjects compared to that implicit in the prediction equation. Although adjustments in the prediction equation could equalize the means for V O2 max and predicted V O2 max, the rather low correlation (r = .55) between these measures precluded the accurate prediction of individual scores. V O2 max remained unchanged with training while submaximal heart rate during bicycle and treadmill exercise showed a significant decrease, resulting in predicted increases in V O2 max in children. Since V O2 max was actually unchanged, the prediction falsely indicated an improvement. Furthermore, despite a significantly lower heart rate in the trained group, there was no difference in predicted V O2 max between the groups post-training. These findings indicate that if V O2 max is the parameter of interest, it would seem to be more satisfactory to measure it directly until more reliable methods of prediction are developed.

Analysis of Variance↗
PubMed2003

[Gene prediction and function research of SARS-CoV(BJ01)].

Through reading the articles, this study points out the shortage of gene prediction and function research about SARS-CoV, and predict it again for developing effective drugs and future vaccines. Using twelve gene prediction methods to predict coronavirus known genes, we select four better methods including Heuristic models, Gene Identification, ZCURVE_CoV and ORF FINDER to predict SARS-CoV(BJ01), and use ATGpr for analyzing probability of initiation codon and Kozak rule, search transcription regulating sequence(TRS) in order to improve the accuracy of predicted genes. Twenty-one probable new genes with more than 50 amino acids have been obtained excluding 13 ORFs which are similar to the genes of NCBI and relative articles. For predicted proteins, we use ProtParam to analyse physical and chemical features; SignalP to analyse signal peptide; BLAST, FASTA to search similar sequences; TMPred, TMHMM, PFAM and HMMTOP to analyse domain and motif in order to improve reliability of gene function prediction. At the same time, we separate the 21 ORFs into four classes using codition of four gene prediction methods, match score, match expection and match length between predicted gene and Coronavirus known gene. In the end, we discuss the results and analyse the reasons.

Computational Biology↗
PubMed2005

Using quantitative CT to predict postoperative pulmonary function in patients with lung cancer.

BACKGROUND: At present, the therapy for patients with lung cancer that achieves a high rate of cure is surgical resection at an early stage of the disease. The aim of this study is to evaluate quantitative computed tomography (QCT) for predicting postoperative pulmonary function in patients with lung cancer. METHODS: The data of thirty-one patients with lung cancer who underwent both pulmonary functional tests and QCT scan before operations were collected. A CT program was used to quantify the volume of whole lung parenchyma with attenuation of -910 HU to -600 HU, which was defined as total functional lung volume (TFLV). Similarly, the volume of lung (lobes or segments) with attenuation of -910 HU to -600 HU was defined as regional functional lung volume (RFLV). Forced vital capacity (FVC), forced expiratory volume in first second (FEV1), FVC% and FEV1% (ratio to reference values of the matched population) were obtained from preoperational pulmonary functional tests. According to the formula: predicted FVC (pre-FVC) = preoperative FVC x [1-(RFLV/TFLV)]; predicted FEV1 (pre-FEV1) = preoperative FEV1 x [1-(RFLV/TFLV)], we obtained values of predicted FVC, predicted FEV1, predicted FVC% (pre-FVC/reference values of the matched population), and predicted FEV1% (pre-FEV1/reference values of the matched population). The paired t test and Pearson correlation test were used to assess significance of differences and correlations between CT predicted values and postoperative measured results of FVC, FEV1, FVC% and FEV1%. RESULTS: QCT predicted values correlated well with postoperative FVC, FEV1, FVC% and FEV1% (r = 0.873, 0.809, 0.849 and 0.801 respectively, all P < 0.01). CONCLUSIONS: QCT is an effective and accurate way to predict postoperative pulmonary function in patients undergoing pulmonary resection, regardless of the patients' preoperative pulmonary functional status.

Female↗
PubMed2005

[A prognostic scoring system for preoperative prediction of lymph node metastases in gastric cancers].

OBJECTIVE: To establish a preoperative scoring system to predict the lymph node metastases (N) in gastric cancers. METHODS: The clinicopathologic data of 291 cases with gastric cancer were analyzed retrospectively. The factors influencing significantly actual lymph node status (pN) were selected through the univariate and the multivariate analysis, and the score of each factor was identified. Scores predicting different N stages were identified using receiver operating characteristic curves. The N stages defined by the score system were compared with the actual pN status using kappa statistics and diagnostic test. RESULTS: Tumor size, depth of invasion and histopathological types were selected to establish the scoring system. According to this score system, scores 0-4 predict N0, scores 5-7 predict N1, scores 8-9 predict N2 and scores 10-13 predict N3. There was a good agreement between N stages predicted by the scoring system and the actual pN status (weighted kappa = 0.605, u = 14.548, P < 0.0001). The crude agreement, positive predictive value and negative predictive value of the scoring system were 82.8%, 65.6% and 88.5%, respectively. CONCLUSION: The scoring system can provide accurate and reliable information to predict the lymph node metastases of gastric cancers preoperatively. It is simple and practical to use in clinical work and can help surgeons to choose an optimal extent of lymph node dissection for gastric cancer.

Humans↗
PubMed2005

Prediction of segmental percent fat using anthropometric variables.

AIM: This study aimed to develop a prediction equation for segmental percent fat from anthropometric measurements. METHODS: The subjects were 107 adults, consisting of 77 males and 30 females, aged from 21 to 82 years. Height, weight, waist circumference, hip circumference, body mass index, waist hip ratio and subcutaneous fat thickness (SFT) were used as anthropometric measurements. The SFTs were measured at 14 sites. Segmental percent fats in both arms (%SF(arms)), both legs (%SF(legs)) and trunk (%SF(trunk)) were measured by dual-energy absorptiometry (DXA) method, and these values were used as references. To predict the segmental percent fat measured by DXA, stepwise multiple regression analysis was conducted using sex, age and the anthropometric measurements as predictors. To examine the systematic error between the observed and predicted values, the error and the observed values were plotted based on Bland-Altman technique, and limits of agreement (LA) were also calculated. RESULTS: The R, SEE and range of LA values in each prediction equation was as follows: %SF(arms): R=0.919, SEE=3.333%, LA=6.5%; %SF(legs): R=0.915, SEE=3.468, LA=6.5%; %SF(trunk): R=0.858, SEE=4.944, LA=9.7%. These prediction equations used 5 to 7 predictors and met the necessary standards for predicting body fat. Although the prediction accuracy of %SF(trunk) was inferior than those of %SF(arms) and %SF(legs), it was superior to those found in previous study reports predicting abdominal visceral fat mass and fat mass at the trunk from anthropometric measurements. CONCLUSIONS: These prediction equations can be considered useful and practical for predicting segmental percent fat and assessing body fat distribution.

Abdominal Fat↗
PubMed1988

Predictive value of Glasgow coma score for awakening after out-of-hospital cardiac arrest. Cerebral Resuscitation Study Group of the Belgian Society for Intensive Care.

The Glasgow coma score (GCS) during days 1-6 after cardiac arrest was used to predict neurological outcome in 360 resuscitated victims of out-of-hospital cardiac arrest. A predictive rule based on the best GCS of 216 patients resuscitated in 1983-84 (prediction group) was constructed, and its predictive power was tested on 133 patients treated in 1985 (test group). Neurological outcome was correctly predicted 2 days after cardiac arrest in 80% of the prediction group, with a best GCS of 10 or above and 4 or below as cutoff points. For patients with a best GCS of 5-9, prediction of outcome was possible 6 days after cardiac arrest, with a best GCS of 8 during the first 6 days as the single cutoff point. The rule was then validated in the test group: the sensitivity was 96%; the specificity 86%; the negative predictive value 97%; and the positive predictive value 77%. These data suggest that this simple GCS-based rule can be helpful in predicting outcome in patients resuscitated after out-of-hospital cardiac arrest, but confirmation of these data is required in a prospective study in a larger number of patients.

Belgium↗
PubMed1980

[Prediction of labor onset and difficulty by the multivariate analysis (author's transl)].

Based on the multivariate analysis, we analysed the weekly change of the antepartum cervical ripe (consistency, effacement, dilatation, position) and the station, then predict the labor onset and difficulty retrospectively. 1) According to longterm observation of the internal examination of 210 primipara, in third trimester, the ripe type increased, and the unripe type decreased are found. Because of the intermediate type increased and unmovable in incidence relatively, so based on the former scoring method of mere addition of scores, it appears ineffective to predict the labor onset. 2) We studied primipara 385 and multipara 434 from 6 weeks before labor. Based on the multiple regression analysis, we could predict the labor onset. The predict value is higher in multipara than primipara, on the primipara, 78.2% antepartum 6 weeks, 82.2% 4 weeks, 92.6% 2 weeks, and on the multipara 84.6%, 82.2%, 96.2% respectively. 3) Based on the method of Mahalanobis' generalized distance, we could predict the labor difficulty in the primipara and multipara. Accuracy of abnormal prediction are 77.1% at 4 weeks formula, 77.2% at 2 weeks formula on primipara; 69.0%, 69.2% respectively on multipara. Accuracy of normal prediction 53.3%, 56.9% on primipara; 30.9% (4 weeks formula) on multipara are found. The abnormal prediction is better than the normal prediction. This method can early predict the labor onset and difficulty than the former scoring method of mere additional scores.

Analysis of Variance↗
PubMed1996

Physician ability to predict appointment-keeping behavior of prenatal patients.

BACKGROUND: Prenatal appointment-keeping is considered an important component of adequate prenatal care. Interventions designed to increase the frequency with which patients keep prenatal appointments would be most effective if directed toward patients at greatest risk of missing prenatal appointments; however, it is difficult to identify this population of patients. The purpose of this study was to determine whether physicians could accurately predict the appointment-keeping behavior of their prenatal patients. METHODS: A simple questionnaire was completed by physicians at the time of the initial visit for prenatal care. On these surveys, physicians made predictions concerning each patients's subsequent appointment-keeping behavior. At the conclusion of prenatal care, predictions were compared with actual appointment-keeping as determined by chart audits. RESULTS: More than one half (57%) of patients kept at least 80% of their scheduled appointments. Physicians predicted fair to poor appointment-keeping for 23% of the patients, but 43% of all patients met the criteria for fair to poor appointment-keeping. There was no correlation between physician predictions and actual appointment-keeping. The physician's sex, level of training, and degree of certainty about his or her predictions had no impact on the accuracy of the predictions. In identifying patients at high risk of missing appointments, physician predictions had a sensitivity of 26%, a specificity of 79%, and a positive predictive value of only 47%. CONCLUSIONS: Physician prediction was not found to be an accurate method of identifying patients at risk for missing appointments.

Adult↗
PubMedDate not supplied

A comparison of predictive and nonpredictive ocular pursuit under active and passive stimulation conditions in humans.

A technique has been developed for the comparison of predictive and nonpredictive ocular pursuit in human subjects, with the objective of estimating the contribution made by predictive processes to the normal pursuit response. Subjects were presented with a target moving at constant velocity in the horizontal plane and instructed to actively pursue the target or to passively stare at it. In the predictive mode (PRD), a step-ramp stimulus with velocity ranging from 12.5 to 50 degrees/s was presented at regular intervals of 1.728 s, in alternating directions, with target exposure durations (PD) that varied from 80 to 640 ms. In the interval between presentations, subjects were in complete darkness. In the nonpredictive mode (RND), similar step-ramp stimuli were presented but with randomized direction and timing of target exposure. In the nonpredictive mode, during both active and passive stimulation, the smooth component of eye velocity was initiated after a mean delay of 125 ms. In the predictive mode, eye velocity started to build up well before target onset, even during passive stimulation. It was found that the time of initiation of this anticipatory response was closely associated with the time at which the target would have changed direction even though the target could not be seen at this time. Eye velocity measured 100 ms after target onset was negligible in the nonpredictive mode, whereas in the predictive mode, it progressively increased with target velocity, reaching a maximum of 18 degrees/s when target velocity was 50 degrees/s and PD was greater than 240 ms. Examination of the difference in the eye velocity trajectories for the predictive and nonpredictive modes indicated that the greatest contribution of prediction occurred approximately 150 ms after target onset and its effects were evident in the predictive response for at least 300 ms. This effect was reflected in the reaction time between target onset and the occurrence of peak eye velocity. In the nonpredictive mode, this progressively increased from 250 ms to 400 ms as PD increased from 80 to 640 ms, whereas in the predictive mode peak velocity occurred an average of 50 ms earlier for all values of PD. The results demonstrate the significant contribution that predictive processes make to normal ocular pursuit behavior and the importance of timing control in this process. They also indicate that this process is not dependent on volitional control, but can be seen as an automatized response during passive stimulation.

Adaptation, Ocular↗
PubMed1997

Outcome prediction of the anterior open bite. Comparison of computer and clinician analysis of cephalograms.

Anterior open bite (AOB) may be expected to close spontaneously in approximately 50% of Caucasian patients. Computer-derived discriminant analysis of a single cephalometric radiograph has been shown to predict closure or non-closure in 88% of patients at the pre-puberal stage, in 74% at the puberal stage, and in 94% at the post-puberal stage. In this study, the predictive capacity of the computer analysis was tested against predictions made by groups of clinicians in Belfast and Toronto. The computer analysis was carried out on the first cephalometric radiographs of a new sample of 34 open bite cases collected serially and recorded over a minimum of 2 years. Thus, the spontaneous outcome was known to the authors. The first radiographs were shown to 20 clinicians in Belfast and 22 in Toronto who were asked to predict the spontaneous outcome. The computerised discriminant analysis made correct predictions in 85% and the clinicians in 64% of the sample. There were no significant differences between the predictions of clinicians in Belfast and Toronto, but computer prediction was more accurate than all grades of clinician. The predictions of qualified orthodontists were generally more accurate than prequalified orthodontists which were more accurate than those of undergraduate dental students but the differences did not rise to the level of statistical significance. For patients at the puberal stage the predictive capacity of qualified orthodontists was less than orthodontists in training. Computer prediction of the spontaneous outcome in open bite improves clinical diagnosis.

Adolescent↗
PubMed2026

CLASPP: A unified model for predicting post-translational modifications.

Post-Translational Modifications (PTMs) are a fundamental mechanism for regulating cellular pathways and increasing the functional diversity of the proteome. Accurately predicting the PTM types that are likely to occur at a given site in the primary sequence is a key challenge in functional proteomics. Existing PTM prediction models predominantly focus on either single PTM types or employ ensemble methods that combine multiple models to predict different PTM types. This fragmentation is largely driven by the vast imbalance in data availability across PTM types, making it difficult to predict multiple PTM types with a single model. To address this limitation, we present the Contrastively Learned Attention-based Stratified PTM Predictor (CLASPP), a unified PTM prediction model. CLASPP addresses imbalance challenges by leveraging unsupervised clustering-based undersampling and a novel contrastive learning framework tailored to PTM data. Additionally, our hierarchical data organization and curation are shown to improve CLASPP's performance by balancing the representation of individual PTM types and provides a standardized dataset to train and validate future model designs. Drawing inspiration from advancements in image and natural language processing, the CLASPP model employs a multi-stage training strategy and a high-quality, curated training dataset to improve PTM prediction performance. To uncover what is learned during the contrastive learning stage, the CLASPP model is shown to distinguish known protein kinase substrate specificity profiles as a form of explainability. Finally, we evaluate the application of CLASPP in predicting PTMs in different model organisms and experimentally validated ubiquitination sites in the understudied DCLK3 kinase. Overall, CLASPP represents a unified model for PTM prediction that addresses key bottlenecks in data imbalance and offers new strategies for biological data curation, thereby improving PTM-type prediction performance across diverse organisms.

Protein Processing, Post-Translational↗
PubMed1997

Prediction of birth weight using the Rossavik growth model: a study in a Dutch population.

OBJECTIVES: To evaluate the Rossavik growth model for predicting birth weight in a Dutch population and to evaluate growth cessation near term. STUDY DESIGN: Birth weight was predicted at various ages between 38 and 42 weeks, menstrual age (MA), and at birth age in 50 normal infants using two sets of ultrasound measurements obtained before 28 weeks, MA. Predicted birth weights were compared to actual weights. The mean percentage difference was used as a measure of systematic error and the standard deviation as a measure of random error. Linear regression analysis was used to evaluate the relationship between percentage differences and birth age. To evaluate the individual growth potential, the Growth Potential Realization Index for weight (GPRIWT) was determined for each fetus. RESULTS: The predictions at 39 and 39.15 weeks, MA, were accurate without systematic error and with a random error of +/-9.3%. Prediction at 38 weeks showed a statistical underestimation (mean +/- SD = -5.8% +/- 8.8), and statistical overestimations were found for predictions after 39.15 weeks and at birth age. A relationship between percentage differences and birth age was not found for predictions between 39.15 and 40 weeks, MA. These findings indicate that growth cessation occurred at 39.15 weeks, MA. Using birth weights predicted at 39.15 weeks, MA, GPRIWT were calculated. The mean GPRIWT value was not significantly different from 100% (p > 0.05), and individual GPRIWT values ranged from 84% to 114%. CONCLUSIONS: The Rossavik growth model can be used to predict birth weight in a Dutch population. However, growth cessation near term appears to occur later than previously reported in other populations.

Birth Weight↗
PubMed2003

Lateral facial soft-tissue prediction model: analysis using Fourier shape descriptors and traditional cephalometric methods.

This study was designed to investigate the relationship between traditional skeletal cephalometric measurement and Fourier analysis of the lateral soft-tissue profile. A random sample of 121 untreated subjects of European descent, with wide ranges of malocclusions and underlying facial patterns, was selected in the Orthodontic Unit at the University of Melbourne. Lateral cephalograms were available for all subjects. Both traditional lateral cephalometric analysis and Fourier soft-tissue profile analysis were carried out. Multivariate statistical analysis among 11 hard-tissue cephalometric measurements and the first 50 Fourier harmonics was then performed. This analysis formed the basis for a subsequently proposed soft-tissue prediction model. From this model, 50 predicted x- and y-harmonics were generated for each subject in the total sample. Calculation of Pearson's correlation coefficients between the actual and predicted harmonics revealed strong relationships for many of the lower-order harmonics. To further test the model, the prediction-coefficients derived from all 121 subjects were then used to make predictions for the first 50 x- and y-harmonics for a subgroup of 10 independent test subjects. Once again, Pearson's correlations between the actual and predicted harmonics of the test model in the lower-order harmonics revealed strong associations. Superimposition of the actual and predicted soft-tissue outlines, however, revealed that much actual detail in the region between the nose and the chin was still lost using the predicted Fourier harmonics. This suggests that soft-tissue prediction based on this Fourier test model, while already useful in Forensic facial reconstruction, may not yet be appropriate for useful diagnosis and planning in clinical disciplines.

Adolescent↗
PubMed1997

Sensitivity and positive predictive value of Medicare Part B physician claims for rheumatologic diagnoses and procedures.

OBJECTIVE: To examine the sensitivity and positive predictive value of Medicare physician claims for select rheumatic conditions managed in rheumatology specialty practices. METHODS: Eight rheumatologists in 3 states abstracted 378 patient office records to obtain information on diagnosis and office procedures. The Medicare Part B physician claims for these patient visits were obtained from the Health Care Financing Administration. The sensitivity of the claims data for a specific diagnosis was calculated as the proportion of all patients whose office records for a particular visit documented that diagnosis and who also had physician claims for that visit which identified that diagnosis. The positive predictive value was evaluated in a separate sample of 331 patient visits identified in Medicare physician claims. The positive predictive value of the claims data for a specific diagnosis was calculated as the proportion of patients with that diagnosis coded in the claims for a particular visit who also had the diagnosis documented in the medical record for that visit. RESULTS: Ninety percent of abstracted office medical records were matched successfully with Medicare physician claims. The sensitivity of the Medicare physician claims was 0.90 (95% confidence interval [CI] 0.85-0.95) for rheumatoid arthritis (RA), 0.85 (95% CI 0.73-0.97) for systemic lupus erythematosus (SLE), and 0.85 (95% CI 0.78-1.0) for aspiration or injection procedures. The sensitivity for osteoarthritis (OA) of the hip or knee was < or = 0.50 if 5-digit codes specifying anatomic site were required. The sensitivity for fibromyalgia (FM) was 0.48 (95% CI 0.28-0.68). The positive predictive values were at least 0.90 for RA, SLE, and aspiration or injection procedures. Positive predictive values for FM and the 5-digit site-specific codes for OA of the knee were 0.83 (95% CI 0.66-1.0) and 0.88 (95% CI 0.75-1.0), respectively, while the positive predictive value of the 5-digit site-specific codes for OA of the hip was zero (95% CI 0-0.26). The positive predictive value of OA at any site was 0.83 (95% CI 0.76-0.90). CONCLUSION: In specialty practice, Medicare physician claims had high sensitivity and positive predictive value for RA, SLE, OA without specification of anatomic site, and injection or aspiration procedures. The claims had lower sensitivity and predictive value for FM and for OA of the hip. The accuracy of Medicare physician claims for other conditions and in the primary care setting requires further investigation.

Aged↗
PubMed2004

Combining evolutionary and structural information for local protein structure prediction.

We study the effects of various factors in representing and combining evolutionary and structural information for local protein structural prediction based on fragment selection. We prepare databases of fragments from a set of non-redundant protein domains. For each fragment, evolutionary information is derived from homologous sequences and represented as estimated effective counts and frequencies of amino acids (evolutionary frequencies) at each position. Position-specific amino acid preferences called structural frequencies are derived from statistical analysis of discrete local structural environments in database structures. Our method for local structure prediction is based on ranking and selecting database fragments that are most similar to a target fragment. Using secondary structure type as a local structural property, we test our method in a number of settings. The major findings are: (1) the COMPASS-type scoring function for fragment similarity comparison gives better prediction accuracy than three other tested scoring functions for profile-profile comparison. We show that the COMPASS-type scoring function can be derived both in the probabilistic framework and in the framework of statistical potentials. (2) Using the evolutionary frequencies of database fragments gives better prediction accuracy than using structural frequencies. (3) Finer definition of local environments, such as including more side-chain solvent accessibility classes and considering the backbone conformations of neighboring residues, gives increasingly better prediction accuracy using structural frequencies. (4) Combining evolutionary and structural frequencies of database fragments, either in a linear fashion or using a pseudocount mixture formula, results in improvement of prediction accuracy. Combination at the log-odds score level is not as effective as combination at the frequency level. This suggests that there might be better ways of combining sequence and structural information than the commonly used linear combination of log-odds scores. Our method of fragment selection and frequency combination gives reasonable results of secondary structure prediction tested on 56 CASP5 targets (average SOV score 0.77), suggesting that it is a valid method for local protein structure prediction. Mixture of predicted structural frequencies and evolutionary frequencies improve the quality of local profile-to-profile alignment by COMPASS.

Algorithms↗
PubMed1993

Predictive performance of population pharmacokinetic parameters of tianeptine as applied to plasma concentrations from a post-marketing study.

The predictive ability of population pharmacokinetic parameters of tianeptine, obtained from a mixed effect analysis of pre-marketing pharmacokinetic studies, was evaluated using tianeptine plasma concentrations obtained during a large multi-center post-marketing surveillance study. The mean prediction error was 7.8 ng.ml-1 and the root mean square prediction error was 52.1 ng/ml when initial estimates of population pharmacokinetic parameters were used to predict drug concentrations in one half of the post-marketing data. When the population parameters were revised to reflect the data collected in the first half of the post-marketing study, the mean prediction error was reduced to -3.2 ng.ml-1 and the root mean square prediction error was reduced to 29.5 ng.ml-1. These results suggest that population pharmacokinetic parameters obtained from pre-marketing data may not accurately predict drug concentrations in patients receiving the drug in the post-marketing setting. Once the population parameters are updated to reflect data from the post-marketing period, the predictive ability of the data-base increases, but substantial variability in the prediction error remains.

Adolescent↗