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PubMed1975

[Pharmacopsychologic studies on normal subjects for the prediction of the therapeutic efficiency of psychotropic drugs].

The paper deals with some basic problems and possibilities of predicting the therapeutic efficiency of psychotropic drugs from studies in normal humans. Comparing drug studies with normal subjects and patients it seems evident that from a methodological and economical point of view studies with normal subjects have many advantages. However, the practical importance of drug studies with normal subjects is limited unless the therapeutic efficiency of a drug can be predicted. There are some arguments which deny the possibility of prediction, e.g. referring to the lack of comparability of dosages, administration regimens, situational parameters and psychosomatic states between normal subjects and patients. Discussing such arguments it is pointed out that perfect comparability of all these factors is not a necessary prerequisite of prediction. A number of theoretically possible models for predicting therapeutic efficiency is suggested. For some of them there already exists some empirical evidence. The first model takes into account the inter- and intra- individual variability of behavior. It is suggested that a state corresponding to the psychosomatic state of patients be approximated, or simulated, in the normal subjects by suitable selection procedures of subjects or by manipulation of the experimental conditions. The usefulness of such a model for predicting therapeutic efficiency has been shown in many studies with anti-anxiety agents. In another model the drug profiles of normal subjects and patients are compared and the prediction is based upon drug effects in normal subjects which can also be seen in patients and which have a high correlation to the patients' improvement. A further model assumes that improvement is partly the result of learning processes. The prediction of therapeutic efficiency is, therefore, based upon the properties of a drug to facilitate or inhibit learning processes. The final two models proposed, predict limitations of therapeutic efficiency. The one model takes into account side-effects; the other the variability of drug response due to situational and person-parameters.

Humans↗
PubMed2002

[Study on a test of screening to predict stroke-using cerebral vascular hemodynamic indexes].

OBJECTIVE: To evaluate efficacy and optimal cut-off-point through cerebral vascular hemodynamic indexes (CVHI) examination to predict stroke. METHODS: A number of 20,333 people at 35 years old and over were checked by CVHI and accumulative score was calculated according to the value of detected indexes. Risk factors of stroke were investigated simultaneously. One hundred and sixty-eight patients with stroke occurred during 4-year following up. Typical syndromes and signs stroke were used as golden standard to evaluate screening efficacy of CVHI. RESULTS: Score of optimal cut-off-point of cerebral vascular hemodynamic indexes was under 75 in ROC curve analyses. Sensitivity, specificity, accuracy, positive and negative predictive values, positive and negative likelihood ratios as well as Youden's index for predicting stroke within 4 years after examination were found to be 87.50%, 67.70%, 67.86%, 2.21%, 99.85%, 2.71, 0.18 and 0.55 respectively. Sensitivity and positive predict values for predicting cerebral vascular thrombosis were superior to predicting cerebral hemorrhage. Positive predicting value in risk exposure population was higher than that of overall population. Coefficiency of variation of cerebral vascular hemodynamic examination was 4.03%. The agreement rate of examination between two physicians was 97.62% and Kappa value was 0.94. CONCLUSION: The score of optimal cut-off-point of cerebral vascular hemodynamic indexes examination was 75. Both Efficacy and reliability for predicting stroke seemed to be good, especially for predicting cerebral vascular thrombosis.

Adult↗
PubMed1976

Predictive tests in Huntington's disease.

HD is a dominantly inherited disorder that affects mental and motor systems and includes a rigid form as well as the better known choreic form. Many articles have been devoted to predicting the future onset of the disease in patients who are at risk, but none of the suggested predictors is currently considered completely reliable. Members from individual families do tend to show a similar age of onset, and similar intellectual and motor abnormalities do develop within a single family; but the presence or absence of this dominant gene of high penetrance is not usually certain until the obvious physical signs appear. Predictive tests are of importance not only to decide which person may develop the disorder, but they may also offer a clue to associated or causal features of the disease. This chapter is a review of reported predictive tests in HD, emphasizing the rationale for their use. Psychological testing has often been abnormal early in the course of the disease of some patients, particularly when motor dexterity or apraxia is tested. Family members often insist that various psychological traits enable them to predict which members are affected by the gene. These opinions are summarized. Neurophysiologic tests are briefly reviewed, including new data on increased liklihood of H-reflexes in HD. Electroencephalography was once touted as a possible predictive test but, although there is frequently an association of a low voltage EEG activity with HD, this change is too variable for certainty in prediction. Pneumoencephalography with specific measurements of caudate atrophy is of clinical interest, but a pneumoencephalogram is rarely needed for diagnosis and caudate atrophy may not actually be an early sign. Metabolic changes in HD include the biochemical effects of hypothalamic dysfunction, changes in growth hormone, and reported change in GABA levels in the CSF or brain. Provocative tests have utilized numerous drugs in an attempt to predict the onset of the disease, including particularly physostigmine and L-DOPA. All of the tests elucidate peculiarities of the disease, and all are of ethical as well as neurological interest. Many of the provocative tests utilize quantification of known neurologic features of the disease, such as reduction in saccadic movements of the eye, increased reflexes, or patterns of movement. The ethical problems in predictive tests, especially tests intended to provoke features of the disease, have been a matter of quiet controversy. Should a nontreatable disease be overtly diagnosed? And if so, will it benefit the patient? Can any of the tests tend to accelerate the patient's decline, either by physical or by psychological trauma? This chapter reviews the various predictive tests and their rationale and concludes that none of the tests are totally reliable. Many offer interesting insights into the effects of HD and do broaden the overall significance of this fascinating disorder of basal ganglion function.

Age Factors↗
PubMed2003

Coding of predicted reward omission by dopamine neurons in a conditioned inhibition paradigm.

Animals learn not only about stimuli that predict reward but also about those that signal the omission of an expected reward. We used a conditioned inhibition paradigm derived from animal learning theory to train a discrimination between a visual stimulus that predicted reward (conditioned excitor) and a second stimulus that predicted the omission of reward (conditioned inhibitor). Performing the discrimination required attention to both the conditioned excitor and the inhibitor; however, dopamine neurons showed very different responses to the two classes of stimuli. Conditioned inhibitors elicited considerable depressions in 48 of 69 neurons (median of 35% below baseline) and minor activations in 29 of 69 neurons (69% above baseline), whereas reward-predicting excitors induced pure activations in all 69 neurons tested (242% above baseline), thereby demonstrating that the neurons discriminated between conditioned stimuli predicting reward versus nonreward. The discriminative responses to stimuli with differential reward-predicting but common attentional functions indicate differential neural coding of reward prediction and attention. The neuronal responses appear to reflect reward prediction errors, thus suggesting an extension of the correspondence between learning theory and activity of single dopamine neurons to the prediction of nonreward.

Animals↗
PubMed2004

The agreement between measured and predicted resting energy expenditure in patients with pancreatic cancer: a pilot study.

OBJECTIVE: To compare measured resting energy expenditure to resting energy expenditure predicted from eight published prediction equations in a sample of patients with pancreatic cancer. DESIGN: Cross-sectional study. SETTING: Ambulatory patients of a tertiary private hospital. PARTICIPANTS: Eight patients with pancreatic cancer (5 males, 3 females; age: 62.0+/- 5.2 years; BMI: 24.4+/- 3.2 kg/m2; weight loss: 12.1+/- 6.0%; mean+/- SD). METHODS: Resting energy expenditure was measured using indirect calorimetry and predicted from eight published prediction methods (Harris-Benedict with no injury factor, Harris-Benedict with 1.3 injury factor, Schofield, Owen, Mifflin, Cunningham, and Wang equations and the 20 kcal/kg ratio). Body composition was assessed by deuterium oxide dilution technique. Statistical analysis was performed by using the method of Bland and Altman, and the Student's t-test. RESULTS: The Harris-Benedict equations with an injury factor of 1.3 resulted in a significantly higher mean predicted resting energy expenditure compared to measured resting energy expenditure, while there was no significant difference between mean measured and predicted resting energy expenditure and the other 7 methods. At an individual level, the limits of agreement are wide for all equations. The best combination of low bias and narrowest limits of agreement was observed in the prediction of resting energy expenditure from the Wang equation (based on fat free mass) and the Harris-Benedict equation (based on weight and height). CONCLUSION: At a group level, there is agreement between mean measured and predicted resting energy expenditure with the exception of the Harris-Benedict equation with an injury factor of 1.3. The results of this pilot study suggest that, for an individual, the limits of agreement are wide, and clinically important differences in resting energy expenditure would be obtained. Clinicians need to be aware of the limitations of the use of resting energy expenditure prediction equations for individuals.

Aged↗
PubMed2005

Prediction of pharmacokinetics and drug-drug interactions from in vitro metabolism data.

There is great interest within the pharmaceutical industry in predicting the in vivo pharmacokinetics (PKs) and metabolism-based drug-drug interactions (DDIs) of compounds from their in vitro metabolism data. Metabolism-based DDIs are largely due to changes in levels of drug-metabolizing enzymes caused by one drug, leading to changes in the PK parameters (mainly clearance) of another. The search for alternative approaches to time-consuming and costly clinical PK drug interaction studies for predicting human DDIs, has been ongoing for decades. In vitro enzyme-mediated biotransformation reactions provide a foundation for predictions that relate PK concepts to enzyme kinetics. This review discusses the principles, assumptions, tools and approaches to in vitro/in vivo prediction, especially in the context of hepatic clearance (the most important PK parameter) and its prediction from in vitro data. Enzyme inhibition is a common cause of DDIs and involves various mechanisms (eg, reversible and mechanism-based inhibition). The models and equations used for predicting DDIs for different types of inhibitor (ie, competitive, partial competitive, non-competitive, partial non-competitive and mixed-type reversible inhibitors, and mechanism-based inhibitors) are extensively presented. Although the methods of prediction are numerous, there remain a number of unresolved factors that may affect the accuracy of the prediction. These factors are also discussed to provide a caution to researchers performing prediction studies.

Algorithms↗
PubMedDate not supplied

Can experts predict health risk from family genograms?

BACKGROUND: The family genogram is sometimes used to aid diagnostic, therapeutic, and preventive care decisions. This study evaluated the efficacy of genograms for predicting health risk in comparison to predictions made using demographic and chart review data. METHODS: Six physicians with expertise in using genograms were asked to evaluate 20 actual patient cases and use three methods to predict the patients' chances, over the next three months, of: a) experiencing illness causing at least one disability day, b) making an unexpected physician visit for a new problem, or c) requiring hospitalization. The three methods were genogram evaluation, review of patient demographics, and review of patients' charts. Predictions from demographic data were always made first; the other two methods were used in varied order. Three months later, actual patient outcomes were reviewed and compared to predictions. RESULTS: Over the next three months, 44% of subjects experienced a disability day, 35% made an unexpected clinic visit, and none required hospitalization. Predictions of these events with genograms were no more accurate than predictions generated from chart review. The six genogram experts did not predict outcomes at better than chance levels. CONCLUSIONS: Genograms may be no more accurate than standard clinical chart review for predicting short-term (three month) health outcomes.

Family Health↗
PubMedDate not supplied

A comparison of neural networks for computing predicted probability of survival for trauma victims.

TRISS is a statistical method for predicting the probability of survival of trauma victims. Analysis of data from the Trauma Registry at Charleston Area Medical Center showed that only 48% of the trauma fatalities in the 5-year period 1992-1996 were correctly predicted by TRISS. Trauma practitioners from other Trauma Centers report similar problems with TRISS. Researchers have suggested improvements that range from simply changing the input variables and/or regression coefficients in TRISS to using an entirely different model. In this study we describe a method of calculating survival probabilities using Artificial Neural Networks (ANN). This method was chosen because of the similarity of the ANN output function to the function that produces the TRISS probability of survival. Additional variables were added based on the results of other research efforts as well as analysis of the CAMC Trauma Registry. A comparison was made between the abilities of TRISS to predict fatalities and to approximate probability of survival. The ANN outperformed TRISS in predicting fatalities in a training set (68.1% correct vs. 47.9% correct) and in a testing set (61.3% correct vs. 51.3% correct). More importantly, the ANN produced better estimates of predicted deaths. Using a data set that included 119 deaths, the ANN model predicted 125 deaths for a 5% relative error. The predicted number using TRISS was 86 for a relative error of 27.7%. Since effective quality improvement for trauma care depends on accurately identifying cases that fall outside the expected results, a more accurate predictive tool allows a more focused review of those significant cases, thus conserving resources without compromising quality. Neural Networks appear to be a predictive tool that can provide probability of survival estimates that are more accurate than TRISS.

Humans↗
PubMed2006

Evaluating the suitability of prediction equations for lung function in Indian children: a practical approach.

OBJECTIVE: Although several prediction equations to evaluate peak expiratory flow rate (PEFR) of Indian children are available in literature, clinicians and researchers need to make a logical choice of which equation to use as reference. The aim was to demonstrate a practical approach to making such a logical choice by using prediction equations on our study population. METHODS: Eighteen linear regression equations generated on Indian children were chosen from available literature. PEFR measured on a Wright peak flow meter on 81 boys and 60 girls, aged between 8 and 13 years, was compared with the predicted values obtained from the equations. Data was systematically analyzed for the extent of over-estimation and under-estimation, correlation between the predicted and measured values and bias and limits of agreement using Bland-Altman plots. RESULTS: The correlation between observed and predicted values using the eighteen equations ranged between 0.616 and 0.797 (for all P < 0.001). The Bland-Altman plots indicated that for all but three equations in boys and three equations in girls, lower measured values of PEFR were associated with higher predicted values. A final choice of a reference prediction equation was based on a combination of factors which included a high correlation between actual and predicted PEFR values, the bias of the estimate, the limits of agreement and the extent to which equations over or under-estimated PEFR. CONCLUSION: A practical approach to evaluate the applicability of prediction equations on an independent data set has been demonstrated.

Adolescent↗
PubMed2006

Improving computational predictions of cis-regulatory binding sites.

The location of cis-regulatory binding sites determine the connectivity of genetic regulatory networks and therefore constitute a natural focal point for research into the many biological systems controlled by such regulatory networks. Accurate computational prediction of these binding sites would facilitate research into a multitude of key areas, including embryonic development, evolution, pharmacogenemics, cancer and many other transcriptional diseases, and is likely to be an important precursor for the reverse engineering of genome wide, genetic regulatory networks. Many algorithmic strategies have been developed for the computational prediction of cis-regulatory binding sites but currently all approaches are prone to high rates of false positive predictions, and many are highly dependent on additional information, limiting their usefulness as research tools. In this paper we present an approach for improving the accuracy of a selection of established prediction algorithms. Firstly, it is shown that species specific optimization of algorithmic parameters can, in some cases, significantly improve the accuracy of algorithmic predictions. Secondly, it is demonstrated that the use of non-linear classification algorithms to integrate predictions from multiple sources can result in more accurate predictions. Finally, it is shown that further improvements in prediction accuracy can be gained with the use of biologically inspired post-processing of predictions.

Algorithms↗
PubMed1988

Predictions of dangerousness: an argument for limited use.

Intense debate has focused on the use of statistical predictions of dangerousness in the criminal justice system. Two conflicting positions maintain wide support: that such predictions are never appropriate in criminal justice decision-making, and that they should be used far more often. Recognizing the fact that implicit and intuitive predictions are made every day in police, prosecutorial, sentencing, and other decisions, and explicit but unscientific predictions are common, this article suggests a theoretical framework justifying limited use of statistical predictions. Statistical predictions may present, in some instances, a morally preferable alternative to biased nonscientific and implicit judgements. Development of a sound jurisprudence of predictions faces major hurdles given the trend toward unscientific predictions in the law and the enormous judicial confusion in dealing with predictions. The concept has contributed to a string of notably poor Supreme Court decisions.

Criminal Law↗
PubMed1988

Comparison of a Bayesian program with three microcomputer programs for predicting gentamicin concentrations.

A recently developed Bayesian regression program was compared with three other aminoglycoside pharmacokinetic dosing programs available for clinical use. From 30 adult patients, 152 measured serum gentamicin concentrations (SGC) were evaluated retrospectively (78 peak and 74 trough). Predictive performance was compared for each method by using the first peak and trough SGC pair to predict subsequent serum concentrations, making a total of 92 predictions (48 peak and 44 trough). The two Bayesian programs (Brater and Koup) were further evaluated using only one initial peak or trough SGC to make the same predictions. Mean predicted error (ME), mean absolute error (MAE), and root mean squared error (RMSE) were calculated for each method. Prediction bias and precision were compared statistically, between each method, by calculating the 95% confidence intervals for the delta ME and delta MAE, respectively. No statistically significant differences were found in the MAEs among any of the methods for predicting peak SGCs, with the exception of the Brater program, using a single trough SGC, which was statistically less precise (less than 0.05). There were few statistically significant differences in the MAEs for trough SGCs; however, Koup's Bayesian program using a single trough concentration yielded statistically more precise predictions than the other methods. The ME was found to differ significantly (p less than 0.05) among estimates for peak and trough SGCs provided by some of the predictive methods.(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed1986

Evaluation of a Bayesian regression-analysis computer program for predicting phenytoin concentration.

A microcomputer program using Bayesian regression analysis to predict serum phenytoin concentrations was evaluated. Phenytoin concentration-time data from nine healthy male volunteers and one male patient were obtained from published studies. For two different dosage regimens that each subject received, the last available predose concentration on the sixth day of the regimen was predicted using observed predose concentrations on both the morning of the third day and on each of the first three days of phenytoin administration. In nine subjects who received at least 10 days of phenytoin therapy, observed concentrations after more than 10 days of therapy were predicted using both one and three observed serum concentrations. Also, in six subjects, the observed predose concentrations for the first three days of an initial phenytoin regimen were used to predict the last predose concentration observed during each subject's second regimen. Predictive performance of the program was evaluated using mean error (m.e.) as a measure of bias, mean absolute error (m.a.e.) as a measure of precision, and root mean square error (r.m.s.e.) as a composite measure of bias and precision. The majority of the predicted serum concentrations were accurate. Predictions of serum concentrations after six days and after more than 10 days of phenytoin therapy were somewhat more accurate when three serum concentrations were used than when only one concentration was used. In the six subjects for whom concentrations from an initial regimen were used to predict those in a second regimen, the largest prediction error was 5 mg/L (m.e. 0.88, m.a.e. 1.9, and r.m.s.e. 2.4).(ABSTRACT TRUNCATED AT 250 WORDS)

Bayes Theorem↗
PubMed1994

Comparative evaluation of body composition methods and predictions, and calculation of density and hydration fraction of fat-free mass, in obese women.

The objective of this study was to apply a three-component model of body composition to a group of obese women in order to (a) establish the relative value of a number of readily available prediction equations by comparison of the extent of agreement between these predictions and body composition estimated by the model and other reference methods and (b) evaluate density and hydration of fat-free mass. Estimation of body composition was carried out by reference methods and prediction equations and the usefulness of these prediction equations for application specifically to obese women was evaluated. The subjects were 15 obese, otherwise healthy, Caucasian women (body mass index > 30kg/m2 and body fat > 40% of body weight, as originally determined using densitometry). Body composition was estimated using three established reference methods (deuterium dilution which primarily measures total body water, densitometry for body fat and fat-free mass and total body potassium) and the three component model constructed from deuterium dilution and densitometry. Density and hydration fraction of the fat-free mass were calculated from appropriate values obtained as integral parts of the three-component model. In addition, body composition was predicted from various prediction equations incorporating weight and height (some of which include a factor for age), from a number of prediction equations utilizing different terms involving the same whole-body bio-electrical impedance measurement and from measurements of skinfold thickness and near infrared interactance. The extent of agreement between methods was assessed using bias and 95% limits of agreement. Mean density of fat-free mass was found to be 1.104 kg/l (s.d. 0.006kg/l) with a range of 1.093 to 1.117 kg/l, and mean hydration fraction was 0.712 (s.d. 0.016) with a range of hydration from 68.2% to 75.1% (all values were calculated from the three-component model). In general, the reference methods (densitometry, deuterium dilution, the three-component model and total body potassium) demonstrated better agreement with each other than with the prediction methods or equations. In these obese women, skinfold thickness measurements are apparently less reliable (large bias and 95% limits of agreement) than in the lean subjects of a variety of other studies. A majority of interpretations of weight and height measurements and predictions incorporating impedance/resistance measurements are apparently not applicable to this group of obese women, due to large values for both bias and 95% limits of agreement.(ABSTRACT TRUNCATED AT 400 WORDS)

Adult↗
PubMed1997

Combination of prostate-specific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer. A multi-institutional update.

OBJECTIVE: To combine the clinical data from 3 academic institutions that serve as centers of excellence for the surgical treatment of clinically localized prostate cancer and develop a multi-institutional model combining serum prostate-specific antigen (PSA) level, clinical stage, and Gleason score to predict pathological stage for men with clinically localized prostate cancer. DESIGN: In this update, we have combined clinical and pathological data for a group of 4133 men treated by several surgeons from 3 major academic urologic centers within the United States. Multinomial log-linear regression was performed for the simultaneous prediction of organ-confined disease, isolated capsular penetration, seminal vesicle involvement, or pelvic lymph node involvement. Bootstrap estimates of the predicted probabilities were used to develop nomograms to predict pathological stage. Additional bootstrap analyses were then obtained to validate the performance of the nomograms. PATIENTS AND SETTINGS: A total of 4133 men who had undergone radical retropubic prostatectomy for clinically localized prostate cancer at The Johns Hopkins Hospital (n=3116), Baylor College of Medicine (n=782), and the University of Michigan School of Medicine (n=235) were enrolled into this study. None of the patients had received preoperative hormonal or radiation therapy. OUTCOME MEASURES: Simultaneous prediction of organ-confined disease, isolated capsular penetration, seminal vesicle involvement, or pelvic lymph node involvement using updated nomograms. RESULTS: Prostate-specific antigen level, TNM clinical stage, and Gleason score contributed significantly to the prediction of pathological stage (P<.001). Bootstrap estimates of the median and 95% confidence interval of the predicted probabilities are presented in the nomograms. For most cells in the nomograms, there is a greater than 25% probability of qualifying for more than one of the pathological stages. In the validation analyses, 72.4% of the time the nomograms correctly predicted the probability of a pathological stage to within 10% (organ-confined disease, 67.3%; isolated capsular penetration, 59.6%; seminal vesicle involvement, 79.6%; pelvic lymph node involvement, 82.9%). CONCLUSIONS: The data represent a multi-institutional modeling and validation of the clinical utility of combining PSA level measurement, clinical stage, and Gleason score to predict pathological stage for a group of men with localized prostate cancer. Clinicians can use these nomograms when counseling individual patients regarding the probability of their tumor being a specific pathological stage; this will enable patients and physicians to make more informed treatment decisions based on the probability of a pathological stage, as well as risk tolerance and the values they place on various potential outcomes.

Decision Support Techniques↗
PubMed1997

Incorporating global information into secondary structure prediction with hidden Markov models of protein folds.

Here we propose an approach to include global structural information in the secondary structure prediction procedure based on hidden Markov models (HMMs) of protein folds. We first identify the correct fold or 'topology' of a protein by means of the HMMs of topology families of proteins. Then the most likely structural model for that protein is used to modify the sequence of secondary structure states previously obtained with a prediction algorithm. Our goal is to investigate the effect on the prediction accuracy of including global structural information in the secondary structure prediction scheme, by means of the HMMs. We find that when the HMM of the predicted topology of a protein is used to adjust the secondary structure sequence predicted originally with the Quadratic-Logistic method, the cross-validated prediction accuracy (Q3) improves by 3%. The topology is correctly predicted in 68% of the cases. We conclude that this HMM based approach is a promising tool for effectively incorporating global structural information in the secondary structure prediction scheme.

Algorithms↗
PubMed1997

Serum estradiol level and oocyte number in predicting severe ovarian hyperstimulation syndrome.

Ovarian hyperstimulation syndrome (OHSS) is a relatively common and potentially life-threatening complication of ovarian stimulation, the pathogenesis of which remains unclear. To clarify the predictive values of serum estradiol levels and oocyte number in severe OHSS, and to investigate the impact of high serum estradiol levels on pregnancy outcome, we retrospectively analyzed clinical data from 431 cycles of ovarian stimulation for assisted reproduction performed from 1993 through 1995. Receiver operating characteristic plots were used to estimate the predictive power of the measured variables. The overall frequency of severe OHSS was 5.5%. Using a serum estradiol level of 3,600 pg/mL as the minimum cut-off value, the sensitivity was 58%, with a specificity of 92%, a positive predictive value of 29%, and a negative predictive value of 97%. The predictive power was similar when a cut-off point of 20 oocytes retrieved was used. The two criteria together gave a sensitivity of 33%, a specificity of 92%, a positive predictive value of 40%, and a negative predictive value of 98%. One of seven oocyte donors developed severe OHSS. The pregnancy rate was higher in patients with severe OHSS than in patients who did not develop this syndrome (73.9% vs 32.5%) but the pregnancy outcomes were not significantly different. We conclude that elevated estradiol concentrations and oocyte number appear to be helpful in predicting severe OHSS, but neither parameter by itself is predictive. This syndrome is rare in the absence of luteal hCG support, either exogenous or pregnancy-derived; when it occurs, there are usually extremely high preovulatory estradiol concentrations and numerous oocytes retrieved. High serum estradiol levels are unlikely to have adverse effects on pregnancy outcome in patients with severe OHSS.

Adult↗
PubMed1998

[Psychophysiologic reactions to predictable aversive stimuli in a delayed conditioning paradigm: reinstatement of the orientating reaction or informational control?].

The preception and orienting response (OR) reinstatement hypotheses are alternative explanations for the reduced responding to predictable as compared to unpredictable aversive stimuli. To test differential predictions from both theories, 60 subjects were presented with 30 stimuli varying in intensity (60 dB(A) vs 100 dB(A)) and predictability (constant vs variable warning) in a 2 x 2 between subject design. Impact ratings, SCR and heart rate were recorded as dependent variables. According to the preception hypothesis a steep and early decrease of responding in the predictable 100 dB(A) condition was expected, whereas according to the OR reinstatement hypothesis a slower decrease with differences between the predictable and unpredictable stimuli at both intensities was hypothesized. To control for response interference only those trials were selected for the analysis for which the interval was the same in the variable and constant warning condition. Results revealed an intensity effect for the SCRs and impact ratings, but no effect of predictability. Although for the heart rate magnitude the intensity by predictability was found in favor of preception, this result appeared to be due to differences in sensitivity between groups during the warning interval. It was concluded that neither hypothesis proved to provide a valid account for the reduced responding to predictable aversive stimuli, but that the data seemed to be most consistent with a safety signal interpretation. Time estimation was considered to be a crucial variable. It is suggested that beyond mere signalling, additional beneficial effects of predictability can be demonstrated in studies where procedures are used which make time estimation unnecessary.

Acoustic Stimulation↗