Utility of an Effect Size Analysis for Communicating Treatment Effectiveness: A Case Study of Cholinesterase Inhibitors for <scp>A</scp> lzheimer's Disease
Bibliographic record
Abstract
OBJECTIVES: To highlight the utility of using an effect size analysis to communicate the effectiveness of treatment interventions. DESIGN: Secondary analysis. SETTING: Previously published systematic review on cholinesterase inhibitors (ChEIs) in Alzheimer's disease. PARTICIPANTS: Individuals with mild to moderate Alzheimer's disease. INTERVENTION: Six-month randomized controlled trials involving a placebo group and a ChEI group (donepezil, galantamine, or rivastigmine). MEASUREMENTS: Cognitive function was assessed according to performance on the cognition subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog). Global Function was quantified using the Clinician's Interview-Based Impression of Change-Plus (CIBIC-Plus). Harm was defined as withdrawal from a trial because of an adverse event. Several effect size indices were computed based on these domains: the success rate difference (SRD), the harm rate difference (HRD), the number needed to treat (NNT) or harm (NNH), and the area under the curve (AUC). Harm:benefit ratios were also computed to compare effect size indices across domains of function. RESULTS: In terms of benefit, the NNT for cognition ranged from 4 to 14 (corresponding AUC values: 0.64-0.54), and the NNT for global function ranged from 6 to 100 (corresponding AUC 0.59-0.51). In terms of harm, the NNH ranged from 6 to 20 (corresponding AUC 0.58-0.53). Only one of the four studies had favorable harm:benefit ratios in both the cognition and global function domains. CONCLUSION: Effect size indices should be reported in clinical trials because they provide important insight into the clinical meaningfulness of results. Additional benefit is gained by comparing effect size indices across domains of function to reveal harm:benefit ratios.
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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 | 0.569 | 0.772 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.022 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".