Systematic Review of Sex‐Specific Reporting of Data: Cholinesterase Inhibitor Example
Bibliographic record
Abstract
OBJECTIVES: To improve the value of research for older adults, we examine sex-specific reporting of data from drug trials for the management of dementia. These data are important because they may influence considerations ranging from the health of populations to shared decision-making by individual patient and caregiver about the risk and benefit of a drug therapy. METHODS: Randomized controlled trials of cholinesterase inhibitors (i.e., donepezil, rivastigmine, or galantamine) with clinical outcomes were identified from searches of MEDLINE, EMBASE, and the Cochrane Library. Sex-specific data were extracted from nine sections (title, abstract, introduction, methods, outcomes, results, discussion, limitations, and conclusion). Among the donepezil trials only, more detailed harms data were obtained. FINDINGS: Thirty-three randomized controlled trials were identified evaluating 15,971 participants (9,103 (57%) female). Trials were highly cited (median citations 158, interquartile range 62-441) and published in high impact journals (median impact factor 7.4, interquartile range 3.4-8.2). Sex was not mentioned in the title, introduction, limitations, or conclusion section of any trial. Only three trials (9%) mentioned sex in the abstract (all as a demographic characteristic), and 8 (24%) in the methods. Almost all (32 (97%)) trials mentioned sex in the results as a demographic variable. One trial reported a sex difference for a secondary outcome. Among the 16 trials studying donepezil, adverse events were frequently reported and often dose-related. No trial provided sex-specific reporting of adverse events. CONCLUSIONS: There is an almost complete lack of sex-specific reporting of data in clinical trials for dementia drug therapies, and no sex-specific reporting of adverse events. Sex-specific reporting of data should be required in drug trials to increase research value and ultimately inform more tailored prescribing for older adults.
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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 | 0.114 | 0.496 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".