Computer-aided assessment of the generalizability of clinical trial results.

BACKGROUND: The effects of an intervention on patients from populations other than that included in a trial may vary as a result of differences in population features, treatment administration, or general setting. Determining the generalizability of a trial to a target population is important in clinical decision making at both the individual practitioner and policy-making levels. However, awareness to the challenges associated with the assessment of generalizability of trials is low and tools to facilitate such assessment are lacking. METHODS: We review the main factors affecting the generalizability of a clinical trial results beyond the trial population. We then propose a framework for a standardized evaluation of parameters relevant to determining the external validity of clinical trials to produce a "generalizability score". We then apply this framework to populations of patients with heart failure included in trials, cohorts and registries to demonstrate the use of the generalizability score and its graphic representation along three dimensions: participants' demographics, their clinical profile and intervention setting. We use the generalizability score to compare a single trial to multiple "target" clinical scenarios. Additionally, we present the generalizability score of several studies with regard to a single "target" population. RESULTS: Similarity indices vary considerably between trials and target population, but inconsistent reporting of participant characteristics limit head-to-head comparisons. CONCLUSION: We discuss the challenges involved in performing automatic assessment of trial generalizability at scale and propose the adoption of a standard format for reporting the characteristics of trial participants to enable better interpretation of their results.

The regression-based discrepancy definition of learning disability: a critical appraisal.

The regression-based discrepancy definition of learning disabilities has been suggested by Rutter and Yule as an improvement of the well-known and much criticized achievement-intelligence discrepancy definition, whereby the examinee's predicted reading attainment is substituted for the intelligence score in the discrepancy expression. Even though the regression-based discrepancy definition has been with us for more than 30 years, critical examination of this approach is scarce. This article fills this lacuna by examining the implications of two variables in the model on the diagnosis of learning disabilities: (a) the effect of predictive validity on the proportion of examinees identified as learning disabled, and (b) the effect of the predictor's identity on the identity of the examinees diagnosed with learning disabilities. Implications of these effects concerning the validity of the regression-based discrepancy model and of the results of its implementation are discussed.

Decision quality (always) increases with the size of information samples-provided that the decision rule is statistically valid: comment on Fiedler and Kareev (2006).

Fiedler and Kareev (2006) showed that small samples can, in principle, outperform large samples in terms of the quality of contingency-based binary choice. The 1st part of this comment critically examines these authors' claim that this small sample advantage (SSA) contradicts Bernoulli's law of large numbers and concludes that this claim is unwarranted. The 2nd part of the comment provides insight into the etiology of the SSA and points to the following as necessary conditions for the SSA's occurrence: (a) the statistical invalidity of the underlying threshold-based decision algorithm and (b) the particular payoff scheme underlying the definition of the decisions' quality. Together, these 2 factors explain how better information provided by larger samples is translated into worse decisions.

The effect of working memory capacity limitations on the intuitive assessment of correlation: amplification, attenuation, or both?

This article challenges Yaakov Kareev's (1995a, 2000) argument regarding the positive bias of intuitive correlation estimates due to working memory capacity limitations and its adaptive value. The authors show that, under narrow window theory's primacy effect assumption, there is a considerable between-individual variability of the effects of capacity limitations on the intuitive assessment of correlation, in terms of both sign and magnitude: Limited capacity acts as an amplifier for some individuals and as a silencer for others. Furthermore, the average amount of attenuation exceeds the average amount of amplification, and the more so, the smaller the capacity. Implications regarding the applicability and contribution of the bias notion in this context and the evaluation of the adaptive value of capacity limitations are discussed.

Dysfunctional implications of narrow window theory: variability in the intuitive assessment of correlation.

Narrow Window theory, suggested by Y. Kareev ten years ago, has so far focused on one central implication of the limited capacity of working memory on intuitive correlation estimation, namely, overestimation of the distal population correlation. This paper points to additional and perhaps more dramatic implications due to the large dispersion of intuitive estimates: (a) large estimation errors, possibly causing overestimation of negligible rhos, misses of strong rhos, and distorted hierarchies of the rhos between different pairs of variables; and (b) large interpersonal differences in the estimation of any given rho and highly incongruent hierarchies of estimated correlations between different pairs of variables. These implications impede both individuals' adaptation to the empirical world and communication among themselves.

Cytokine levels in gingival crevicular fluid of erupting primary teeth correlated with systemic disturbances accompanying teething.

PURPOSE: The aim of this study was to investigate whether there are increased levels of the inflammatory cytokines IL-1beta, IL-8, and TNFalpha in the gingival crevicular fluid (GCF) of erupting primary teeth. This increase could explain such clinical manifestations as fever, diarrhea, increased crying, and sleeping and eating disturbances that occur at this time. METHODS: Sixteen healthy children aged 5 to 14 months (mean=9.8 months) were examined twice a week over 5 months. Gingival crevicular fluid samples were taken from erupting teeth. As a control, GCF was collected from the same teeth 1 month later. Cytokine production was measured by ELISA. Signs and clinical symptoms were listed. Pearson correlation coefficients were used in the comparisons described below. A paired t test was used to analyze the same variable at different times. RESULTS: Fifty teeth of the 16 children were studied. GCF samples were collected from 21 of these teeth. Statistically significant differences (P<.05) were found with regard to the occurrence of fever, behavioral problems, and coughing during the teething period and the control period. During the control period, 72% of the children did not exhibit any clinical manifestations, whereas during the teething period only 22% of the children did not exhibit any clinical manifestations. The study revealed high levels of inflammatory cytokines during the teething period, with a statistically significant difference in TNFalpha levels (P<.05) between the teething period and the control period. Correlations were found between cytokine levels and some of the clinical symptoms of teething: IL-1beta and TNFalpha were correlated with fever and sleep disturbances; IL-beta and IL-8 were correlated with gastrointestinal disturbances; IL-1beta was correlated with appetite disturbances. CONCLUSIONS: Cytonkines appear in the GCF of erupting prmary teeth. The cytokine levels are correlated to some symptoms of teething.