Sex/gender and additional equity characteristics of providers and patients in perioperative anesthesia trials: a cross-sectional analysis of the literature
Bibliographic record
Abstract
Sex and gender, among other equity-related characteristics, influence the process of care and patients’ outcomes. Currently, the extent to which these characteristics are considered in the anesthesia literature remains unknown. This study assesses their incorporation in randomized controlled trials (RCTs) on anesthesia-related interventions, for both patients and healthcare providers. This is a cross-sectional analysis using an existing dataset derived from the anesthesia literature. The dataset originated from a scoping review searching MEDLINE, Embase, CINAHL, CENTRAL, and the Cochrane Database of Systematic reviews. RCTs investigating the effect of anesthesia-related interventions on mortality for adults undergoing surgery were included. Equity outcome measures were recorded for both patients and providers and assessed for inclusion in the study design, reporting of results, and analysis of intervention effects. Three-hundred sixty-one RCTs (n = 144,674) were included. Most RCTs (91%) reported patient sex/gender, with 58% of patients identified as male. There were 139 studies (39%), where 70% or more of the sample was male, compared to just 14 studies (4%), where 70% or more of the sample was female. Only 10 studies (3%) analyzed results by patient sex/gender, with one reporting a significant effect. There was substantial variation in how age was reported, although nearly all studies (98%) reported some measure of age. For healthcare providers, equity-related information was never available. Better consideration of sex/gender and additional health equity parameters for both patients and providers in RCTs is needed to improve evidence quality, and ultimately, patient care and outcome.
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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 | 0.198 | 0.399 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".