P3‐375: A multidimensional effect‐size analysis of cholinesterase inhibitors for Alzheimer's disease
Bibliographic record
Abstract
The purpose of this study was to compute several different effect size indices on the same set of cholinesterase inhibitor (ChEI) trials in Alzheimer's disease (AD) in an effort to better evaluate the clinical significance of these drugs, and to underscore the usefulness adopting a multidimensional effect size approach in this field. The present study is a secondary analysis of the Cochrane Systematic Review published by Birks (2006). The following effect size measures were computed: (1) the magnitude of difference between drug and placebo groups on the ADAS-Cog was assessed using Cohen's D; (2) the number of patients needed to treat (NNT) to get one improvement on the Clinician's Interview-Based Impression of Change (CIBIC-Plus); (3) the number of patients needed to treat to get one patient to withdraw from a study due to an adverse event (the number needed to harm or NNH); and (4) the area under the curve (AUC) was calculated for the CIBIC-Plus and for the number of patients who withdrew due to an adverse event. The AUC estimates the probability that if you sampled 1 patient from the drug group and 1 from the placebo group, the outcome of the patient from the drug group would be better, or worse, than that of the patient in the placebo group. We examined the 8 studies reported in Birks (2006) for which data were available to compute all of these effect size indices. The average Cohen's D value was 0.37, suggesting a small benefit of the ChEIs on cognition. In terms of improvement on the CIBIC-Plus, the NNT was 13, but the AUC was only .54. With respect to the number of withdrawals due to an adverse event, the NNH was 8 and the AUC was .57. Although Cohen's D suggested some benefit of the ChEIs at the group-level, effect size indices that provide information at the patient-level (i.e., NNT, NNH, AUC) suggested no benefit or even some harm associated with the ChEIs. These results underscore the need to consider multiple effect size indices when evaluating the clinical significance of a particular treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.208 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.036 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".