[Comparison of 2 related random samples: predictive test and Wilcoxon predictive range test].
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Analysis of Variance↗
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Short-term assays to predict carcinogenicity. In-vitro assays that may be predictive of tumour-promoting agents.
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Animals↗
[Loss of predictability and experimental neurosis: solution of simple numerical problems as a function of variation in the predictability of a stressor].
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Arousal↗
[Pulmonary function in healthy subjects and its prediction. 6. Nomogram for measuring of the predicted values of spirogram, subdivisions of lung volume and pulmonary diffusing capacity in adults (author's transl)].
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Adult↗
The effect of prediction accuracy on choice reaction time.
In this study, we examined the effect of prediction accuracy on reaction time (RT). Subjects performed on three blocks of choice RT trials, all of which involved the mapping of four stimuli (red, green, 1, or 0) onto two response keys. The subjects were told that the four stimuli were equally probable and that their task was to respond to each stimulus onset by pressing the correct key. In one block (stimulus-prediction), the subjects predicted, prior to each trial, the precise stimulus that would appear. In a second block (category-prediction), the subjects predicted the category of the stimulus (i.e., color or digit) that would appear. In a third block (no-prediction), the subjects simply responded to each stimulus without making a prior prediction. In the stimulus-prediction block, RT was faster for correct predictions than for incorrect predictions. In addition, RT was faster on trials in which an incorrect prediction involved the correct category than on trials in which it involved the incorrect category: that is, a "half-wrong" prediction was better than an "all-wrong" prediction. In the category-prediction block, RT was faster when the stimulus category was predicted correctly than when it was not. There was little evidence of a response-facilitation contribution to the correct-prediction effect. These results permit inferences concerning the encoding and organization of information in memory.
Adult↗
Predicting outcome in coronary disease. Statistical models versus expert clinicians.
To study the accuracy with which long-term prognosis can be predicted in patients with coronary artery disease, prognostic predictions from a data-based multivariable statistical model were compared with predictions from senior clinical cardiologists. Test samples of 100 patients each were selected from a large series of medically treated patients with significant coronary disease. Using detailed case summaries, five senior cardiologists each predicted one- and three-year survival and infarct-free survival probabilities for 100 patients. Fifty patients appeared in multiple samples for assessing interphysician variability. Cox regression models, developed using patients not in the test samples, predicted corresponding outcome probabilities for each test patient. Overall, model predictions correlated better with actual patient outcomes than did the doctors' predictions. For three-year survival, rank correlations were 0.61 (model) and 0.49 (doctors). For three-year infarct-free survival predictions, correlations with outcome were 0.48 (model) and 0.29 (doctors). Comparisons by individual doctor revealed Cox model three-year survival predictions were better than those of four of five doctors (model predictions added significant [p less than 0.05] prognostic information to the doctor's predictions, whereas the converse was not true). For infarct-free survival, the Cox model was superior to all five doctors. Where predictions were made by multiple doctors, the interphysician variability was substantial. In coronary artery disease, statistical models developed from carefully collected data can provide prognostic predictions that are more accurate than predictions of experienced clinicians made from detailed case summaries.
Coronary Disease↗
A time-insensitive predictive instrument for acute myocardial infarction mortality: a multicenter study.
This study develops a "time-insensitive" predictive instrument for acute myocardial infarction mortality that would be useful both as a real-time clinical decision aid in the emergency medical setting and also for retrospective assessment and comparison of medical care based on risk-adjusted mortality predictions. This was done using prospectively-collected data on 5,773 patients with chief complaints of chest pain or other symptoms suggesting acute cardiac ischemia who came to six New England hospitals over a 2-year period. In phase one, based upon 4,099 patients, multivariate logistic regression was used to develop the predictive instrument. In phase two, its accuracy and diagnostic performance were tested on an independent sample of 1,387 patients presenting with symptoms compatible with acute cardiac ischemia. Discrimination between patients who lived and those who died was reflected by receiver-operating characteristic (ROC) curve areas of 0.85, 0.80, and 0.76, respectively, for all emergency department study subjects regardless of final diagnosis, subjects who proved to be having acute cardiac ischemia, and subjects who proved to be having acute infarction. Good calibration was shown by the fact that the predicted mortality was found to not vary significantly from actual mortality rates across deciles of predicted probabilities from 0% to 100%. In phase three, based on all 945 study subjects with acute myocardial infarction, each of the six hospitals' actual mortality rates were compared to their rates predicted by the predictive instrument. Actual hospital mortality rates ranged from 9.9% to 19.3%, with one hospital having a significantly higher rate (P = 0.005) and two hospitals both). Predicted mortality rates ranged from 13.4% to 19.4%, with one hospital having a significantly higher predicted rate (P = 0.005) and two hospitals having significantly lower predicted rates (P = 0.04 and P = 0.03). Individual hospitals' differences between actual and predicted mortality ranged from -3.4% to +3.1% (all NS). When grouped by hospital type, the actual mortality rates were 14.9%, 17.3%, and 13.0%, respectively, for urban teaching, smaller city teaching, and rural nonteaching hospitals (all NS). The predicted mortality rates were 16.5%, 17.1%, and 13.6%, respectively, with the rate for rural nonteaching hospitals being significantly lower (P = 0.009). No hospital type had significant differences between their actual and predicted mortality rates (NS). The time-insensitive predictive instrument for acute infarction mortality shows potential for risk-adjusted studies of hospitals mortality for multihospital groups, hospital-to-hospital comparisons, and within-hospital assessment.(ABSTRACT TRUNCATED AT 400 WORDS)
Adult↗
Prediction of motor status 3 and 6 months post severe traumatic brain injury: a preliminary study.
The prediction of outcome following severe traumatic brain injury has received considerable attention in recent years. Previous prediction studies have focused on a long-term follow-up or prediction period. The reported outcome measures generally adopted a global approach (e.g. independent living) in terms of the prediction of physical function. The objective of the present study was to construct clinically useful predictive equations of motor system status, as represented by selected postural reactions (indicators of central nervous system function). Specifically, these equations would serve to predict the recovery of equilibrium and protective reactions both at 3 and 6 months post-injury, respectively. A stepwise multiple logistic regression analysis was performed, where nine predictive variables were considered using a multivariate approach. The results indicate that coma duration followed by age contribute significantly to the predictive capability of the models at both 3 and 6 months post-injury. Specifically, at 3 months, the predictive variables 'coma duration' and 'age' enabled an 84.62% correct prediction rate, whereas, at 6 months, 'coma duration' and 'age' enabled a 79.49% correct prediction rate. In addition, the exact probabilities (for given sample ages and coma durations) and associated 95% confidence intervals were calculated based on the predictive models obtained. The theoretical framework underlying these predictive models can form the basis for further studies. Furthermore, these preliminary predictive models have potential implications for early treatment planning and patient management.
Adult↗
Predictions of epidemiology and the evaluation of cancer control measures and the setting of policy priorities.
Cancer incidence predictions may be constructed for administrative and scientific purposes. For administrative purposes it is often important that the predictions come true. The resources planned on the basis of the predictions and allocated on the diagnostics, treatment and rehabilitation can then be optimally utilized. However, predictions that do not materialize can also be useful. The effects of intervention or early detection programmes express themselves as failures of predictions that have been made in the absence of such programmes. Predictions of cancer incidence in Finland are used as examples. The prerequisite for the predictions is a well-functioning population-based cancer registry. The predictions were constructed using time trends and differentials in cancer incidence with or without the aetiological or other risk factors. Short-term, 10-15 year predictions with no explicit use of risk factors, have proven successful with most cancers, e.g. those of the colon, rectum, pancreas and urinary organs, and lymphomas. The marked prediction failures have occurred for cancers of the lung and breast. Predictions for these cancers have been improved by taking aetiological or other risk factors explicitly into account. The cancer consequences of the preventive cardiovascular programme in North Karelia have been evaluated using predictions. The effectiveness of screening for cervical cancer at population level was predicted on the basis of estimated parameters of the natural history of the disease.(ABSTRACT TRUNCATED AT 250 WORDS)
Adult↗
Our surprising inability to predict the outcomes of psychological treatments--with special reference to treatments for drug abuse.
Our ability to predict the outcome of psychological treatments, particularly for drug dependence, is examined by (1) new data on a VA sample, (2) a review of studies predicting the outcome of drug abuse treatment, and (3) a review of predicting the outcome of psychotherapy for other types of patients. For (1), the direct predictions of 13 staff members' ratings of the outcome of treatment correlated .27 with the outcome. Although staff predictions improved when grosser predictions were examined, the results are disappointing given the level of discrimination required and the modest levels of prediction attained. For (2), prediction success with drug abuse patients in other studies were also disappointing and the bases for predictions often did not hold up on crossvalidation. For (3), direct predictions in the Penn Psychotherapy Study were similar to the VA sample in level of success. Other studies produced insignificant or similarly low levels of prediction success. In our discussion we suggest that rather than relying mainly upon pretreatment status for prediction, two promising areas should be examined in future studies: (1) the patient's early response to the treatment and to the therapist, (2) the patient's environment posttreatment that should be altered to consolidate and perpetuate the gains made during treatment. In conclusion, it is true that we are surprisingly unable to predict the outcomes of psychological treatments beyond a very modest level.
Adult↗
Clinical experience and predicting survival in coronary disease.
To study the accuracy with which long-term prognosis can be predicted in patients with coronary artery disease, prognostic predictions obtained from a large, diverse sample of practicing cardiologists were compared with predictions from a multivariable statistical model. Test samples of 10 patients each were selected from a large series of medically treated patients with significant coronary disease. Using detailed clinical summaries, 49 cardiologists each predicted the probability of 3-year survival and infarction-free survival for 10 patients. Cox regression models, developed using patients who were not in the test samples, were also used to predict corresponding outcome probabilities for each test patient. Overall, the model estimates of prognosis were significantly better than the doctors' predictions. The rank correlation of model predictions with 3-year survival was 0.60, compared with 0.52 for the physicians. Model predictions added significant prognostic information to the doctors' predictions, whereas the converse was not true. Where predictions were made by multiple doctors, the inter-physician variability was substantial. Neither practice characteristics nor extent of clinical experience significantly affected the physicians' predictive accuracy. In coronary artery disease, statistical models developed from carefully collected data can provide prognostic predictions that are more accurate than predictions of experienced clinicians made from detailed case summaries.
Coronary Disease↗
Impact of personalised risk predictions on breast cancer risk perceptions: insights from the BREATHE study.
OBJECTIVE: Biennial mammography screening is well-established for women aged 50 and above, but guidelines for younger women are less clear. Risk-based screening may provide women with key information to make informed decisions about their breast cancer risk and screening. This study examines how predicted breast cancer (BC) risk shapes women's perception and confidence in risk prediction. METHODS: Women aged 35 to 59 years were recruited for a prospective multi-centre cohort and stratified into above-average, average, or below-average BC risk categories based on genetic and non-genetic risk factors. Perceived risk was assessed at enrolment and after participants were informed of their predicted risk. We used ordinal models to identify predictors of perceived risk and logistic regression to examine the relationship between changes in perceived risk and confidence in the risk prediction. RESULTS: At enrolment, 43% and 47% of 4112 participants perceived their BC risk pre-result as low or average, respectively. Thirty-five percent adjusted their perceived risk to align more closely with their predicted risk. Predictors of perceived risk post-result: perceived risk pre-result, predicted risk, ethnicity and having regular menstruation. Participants who underestimated their BC risk were nearly eight times more likely to have low confidence in the accuracy of their predicted risk (OR for underestimation vs. accurate perception: 7.94 [95% CI 5.60-11.28]). Predictors of perceived risk post-result: perceived risk pre-result, predicted risk, ethnicity and having regular menstruation. Confidence in risk prediction was lowest when women's perceived risk pre-result was lower than their predicted risk (OR-2 vs 0 [95%CI] 5.06 [3.67 to 6.97]). CONCLUSION: Many women underestimated their BC risk, and their initial perceptions were influenced by the knowledge of their predicted risk. Women who underestimated their risk had less confidence in their predicted risk scores.
Humans↗
Prediction of nuclear maturity from cumulus-coronal morphology: influence of embryologist experience.
PURPOSE: A majority of in vitro fertilization (IVF) programs continues to evaluate oocyte maturity on the basis of cumulus-coronal morphology (CCM) even though marked asynchrony has been reported between CCM and nuclear maturity. This study was designed to examine changes in embryologists' ability to correctly predict nuclear maturity from CCM as a function of increasing experience. Nuclear maturity was assessed by inverted microscopy with a modified spreading technique at follicular aspiration. A second objective was to determine the percentage of oocytes which displayed asynchrony between CCM and nuclear maturity as assessed by embryologists with extensive experience in oocyte maturity evaluation. RESULTS: The three participating embryologists had directly evaluated 1304, 75, and 0 oocytes for nuclear maturity and CCM at study initiation and correctly predicted nuclear maturity from CCM in 74, 64, and 47% of oocytes, respectively. Embryologist 1 did not significantly change in predictive ability during the 17-month study period. Embryologist 2 significantly improved in predictive ability during the first 9 months of the study (841 oocytes evaluated) and plateaued thereafter, at a similar percentage of correct predictions as embryologist 1. Embryologist 3 continued to improve in predictive ability throughout the study period, reaching 61% correct predictions at the close of the study after evaluating 223 oocytes. Once embryologists had plateaued in their predictive ability, 72% of oocytes evaluated received the correct nuclear maturity classification based on CCM. Significantly fewer oocytes (54%; 375/690) evaluated by embryologists who had not plateaued in their predictive ability received the correct nuclear maturity classification based on CCM. CONCLUSIONS: These results indicate that embryologists' ability to predict oocyte nuclear maturity correctly from CCM continues to change over several months even when pretraining video recordings are used before beginning direct evaluations. After embryologists plateaued in their predictive ability, nuclear maturity still could not be correctly predicted from CCM in 28% of oocytes due to asynchrony between nuclear and CCM maturity. Based upon this, circumstances in which the spreading technique should be used for direct assessment of nuclear maturity as opposed to assessment of CCM only are discussed.
Cell Differentiation↗
Effect of epinephrine on the ability of end-tidal carbon dioxide readings to predict initial resuscitation from cardiac arrest.
OBJECTIVE: To determine if the administration of epinephrine changes the partial pressure of end-tidal CO2 during cardiac arrest, as previously reported. Such a change could diminish the demonstrated ability of end-tidal CO2 measurements to predict resuscitation from cardiac arrest. DESIGN: The partial pressures of end-tidal CO2 of adult cardiac arrest patients who received i.v. epinephrine in doses from 1 to 15 mg were monitored throughout arrest. SETTING: Emergency department of a university hospital. PATIENTS: Adults (n = 64) in cardiac arrest with a mean age of 70 +/- 12 yrs, of whom 35 were males and 15 had a mean time of return of spontaneous circulation of 6.5 +/- 11 hrs. INTERVENTIONS: End-tidal CO2 (in torr) was analyzed on arrival, before the first dose of epinephrine, and 4 mins after epinephrine was administered in varying doses chosen by the supervising physician. MEASUREMENTS AND RESULTS: The end-tidal CO2 decreased an average of 0.3 torr (0.04 kPa) after epinephrine was administered. Patients with a return of pulse had a decrease of -2 torr (-0.3 kPa) vs. an increase of 0.3 torr (0.04 kPa) for those patients with no return of pulse (p = .07). In 33% of patients, there was no change; in 28%, the partial pressure of end-tidal CO2 increased, and in 39%, it decreased. There was no correlation between the change in end-tidal CO2 after epinephrine and whether or not patients regained a pulse (r2 = .08, p = .07), although a decrease in end-tidal CO2 was most often associated with return of pulse. At a threshold of 10 torr (1.3 kPa), the first end-tidal CO2 had a positive predictive value for return of pulse of 50% and a negative predictive value of 82%. Just before epinephrine administration, the positive predictive value was 71% and the negative predictive value was 83%; 4 mins after epinephrine administration, the positive predictive value was 64% and the negative predictive value was 86%. A decrease in end-tidal CO2 after epinephrine had a positive predictive value of 53% and a negative predictive value of 92%. End-tidal CO2 readings predicted resuscitation most accurately when taken after initial stabilization and before administration of epinephrine. CONCLUSIONS: Although epinephrine administration may decrease end-tidal CO2 tensions in cardiac arrest, it does so unpredictably in individual patients, and it does not eliminate the predictive value of this measurement.
Aged↗
An assessment of protein secondary structure prediction methods based on amino acid sequence.
Five of the several secondary structure prediction methods based on protein amino acid sequence has been computerized, allowing the calculation of joint prediction histograms which have been shown to be superior to any individual prediction. The known structures of about 40 proteins experimentally determined by X-ray crystallography are compared with the predictions resulting from calculated histograms. The accuracy of the predictions for helices is generally much better than for both beta-sheet regions and for turns. The overall agreement between prediction and observation within the amino terminal half of the protein molecules is clearly superior to that for the carboxyl half, suggesting an amino nucleating core. Predictions for smaller proteins and thermally stable proteins are generally good, indicating the sensitivity of the methods to short-range but not long-range interactions. In less than half the cases tested were the predictions useful; there was no way of knowing ahead of time if a favorable prediction would result. Given the lack of dramatic improvement with an increase in data base for the schemes and the generally poor agreement factors, it appears that a perfect predictive algorithm must include a consideration of energy minimization, thermalization, and long-range interactions. Extreme caution is suggested in applying present prediction routines to unknown protein structures.
Amino Acid Sequence↗
Protein structure prediction.
Current methods developed for predicting protein structure are reviewed. The most widely used algorithms of Chou and Fasman and Garnier et al for predicting secondary structure are compared to the most recent ones including sequence similarity methods, neural network, pattern recognition or joint prediction methods. The best of these methods correctly predict 63-65% of the residues in the database with cross-validation for 3 conformations, helix, beta strand and coli with a standard deviation of 6-8% per protein. However, when a homologous protein is already in the database, the accuracy of prediction by the similarity peptide method of Levin and Garnier reaches about 90%. Some conclusions can be drawn on the mechanism of protein folding. As all the prediction methods only use the local sequence for prediction (+/- 8 residues maximum) one can infer that 65% of the conformation of a residue is dictated on average by the local sequence, the rest is brought by the folding. The best predicted proteins or peptide segments are those for which the folding has less effect on the conformation. Presently, prediction of tertiary structure is only of practical use when the structure of a homologous protein is already known. Amino acid alignment to define residues of equivalent spatial position is critical for modelling of the protein. We showed for serine proteases that secondary structure prediction can help to define a better alignment. Non-homologous segments of the polypeptide chain, such as loops, libraries of known loops and/or energy minimization with various force fields, are used without yet giving satisfactory solutions. An example of modelling by homology, aided by secondary structure prediction on 2 regulatory proteins, Fnr and FixK is presented.
Algorithms↗
Epstein-Barr virus genome may encode a protein showing significant amino acid and predicted secondary structure homology with glycoprotein B of herpes simplex virus 1.
We report significant sequence and predicted secondary structure homology between the herpes simplex virus 1 glycoprotein B (gB) and a protein predicted to be encoded by the BALF4 reading frame of Epstein-Barr virus (EBV). Homology was detectable at the DNA level and was highly significant at the protein level and when evolutionary substitution frequencies of amino acids in related proteins were taken into account. Hydropathic analyses predicted that the two proteins possess conserved N-terminal and C-terminal hydrophobic domains. The N-terminal hydrophobic domains share features in common with known cleavable membrane insertion signal sequences. The amino acid sequences of the C-terminal hydrophobic domains predict three adjacent membrane-spanning segments as had been previously predicted for gB. In an alignment of the two amino acid sequences, 247 of 903 gB residues had a matched pair in the BALF4 sequence, and 247 of 854 BALF4 residues were found to have a matched pair in the gB sequence. In addition, all 10 cysteine residues located outside the predicted signal sequence of both proteins were conserved, as were four predicted N-linked glycosylation sites. In all, 43% of the residues in the aligned sequences are predicted to possess equivalent secondary structures. gB is a virion envelope glycoprotein required for virus entry into cells. The domain of gB determining the rate of entry into cells has been mapped; the predicted structure of this domain in gB and the predicted EBV protein are almost identical. Similarly, the cytoplasmic domain of gB postulated to interact with submembrane proteins was also nearly identical in predicted structure to that of the EBV protein. These results suggest that EBV encodes a protein similar in structure and function to the herpes simplex virus 1 gB.
Amino Acid Sequence↗
Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.
AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.
Humans↗