Sex differences in patterns of potentially inappropriate prescribing and adverse drug reactions in hospitalized older people: Findings from the <scp>SENATOR</scp> trial
Bibliographic record
Abstract
BACKGROUND: Older women experience more adverse drug reactions (ADRs) than older men. However, the underlying basis for this sex difference is unclear. Sex (biological status) and/or gender (sociocultural constructs) influences on patterns of inappropriate prescribing in multimorbid older adults may be one reason for this ADR sex difference. In this secondary analysis, we examined whether incident ADR sex differences could be related to concurrent sex differences in potentially inappropriate prescribing. DESIGN AND SETTING: A retrospective secondary analysis of sex differences in the prevalence of potentially inappropriate medications (PIMs), potential prescribing omissions (PPOs), and ADRs among the 1537 participants (47.2% female, median [IQR] age 78 [72-84] years) was undertaken in the SENATOR clinical trial database, conducted in six large European medical centers. PARTICIPANTS AND METHODS: We looked specifically for male/female differences relating to PIMs and PPOs (defined by STOPP/START version 2 criteria) identified within 48 h of acute hospitalization. We also assessed sex differences for ADRs identified at 14 days from admission or discharge, whichever came first. ADRs were assessed by blinded endpoint adjudication panel consensus. RESULTS: During hospitalization, significantly more females experienced ≥1 ADR compared to males (28% and 21%, respectively; odds ratio 1.40, 95% CI 1.10-1.78, p < 0.005). Nine of the 11 STOPP-criteria PIMs showing a significant sex difference occurred more often in females. Of the four START-criteria PPOs showing a significant sex difference, all occurred more often in females. Some sex-associated PIMs reflect higher prevalence of related conditions in older women. CONCLUSION: We conclude that specific STOPP-criteria PIMs and START-criteria PPOs were identified more frequently in older women than older men during acute hospitalization, possibly contributing to higher ADR incidence in older women. Prescribers should appreciate sex differences in exposure to potentially inappropriate prescribing and ADR risk, given the preponderance of older women over older men in most clinical settings.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".