The association of female sex with application of evidence-based practice recommendations for perioperative care in hip fracture surgery
Bibliographic record
Abstract
BACKGROUND: Sex and gender inequality is prevalent in health care, and affects receipt of health care services and outcomes. Our objective was to measure the association between sex and receipt of evidence-based perioperative care for hip fracture in Ontario. METHODS: This was a population-based retrospective cross-sectional analysis. We identified all Ontario residents aged 66 years and older who had hip fracture surgery between 2014 and 2016. After protocol registration, we measured the adjusted association between female sex and perioperative geriatric care (primary outcome), anesthesia consultations, regional analgesia and neuraxial anesthesia (secondary outcomes) using multilevel multivariable adjusted logistic regression. Pre-specified sensitivity analyses were also performed. RESULTS: We identified 22 661 patients who had hip fracture surgery; 16 162 (71.3%) were women. Women were less likely to receive perioperative geriatric care (adjusted odds ratio [OR] 0.80, 95% confidence interval [CI] 0.72 to 0.88) and anesthesia consultations (adjusted OR 0.89, 95% CI 0.80 to 0.98); women were more likely to have timely surgery (adjusted OR 1.26, 95% CI 1.17 to 1.36). Receipt of neuraxial anesthesia (adjusted OR 0.98, 95% CI 0.93 to 1.04) and regional analgesia (adjusted OR 1.00, 95% CI 0.94 to 1.07) were not different between sexes. INTERPRETATION: More than 2 out of 3 patients who had hip fracture surgery were women; however, women were less likely to receive perioperative geriatric care and anesthesia consultations. Given the effectiveness of these interventions for improving outcomes, population-level hip fracture outcomes may be improved by decreasing sex-based disparities in application of evidence-based recommended perioperative care. Protocol registration:ClinicalTrials.gov, no. NCT03422497
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".