Gender disparities in postoperative outcomes following elective spine surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Several studies have described disparities between male and female patients following spine surgery, but no pooled analyses have performed a robust review characterizing differences in postoperative outcomes based on gender. The purpose of this study was to broadly assess the effects of gender on postoperative outcomes following elective spine surgery. METHODS: Between November 2022 and March 2023, PubMed, MEDLINE, ERIC, and Embase were queried using artificial intelligence-assisted software for relevant cohort studies. Cohort studies with a minimum sample of 100 patients conducted in the United States since 2010 were eligible. Studies related to trauma, tumors, infections, and spinal cord pathology were excluded. Independent extraction by multiple reviewers was performed using Nested Knowledge software. A fixed- or random-effects model was used if heterogeneity among included studies in a meta-analysis was < 50% or ≥ 50%, respectively. Risk of bias was assessed independently by multiple reviewers using the Newcastle-Ottawa Scale. Pooled effect sizes were calculated for readmission, nonroutine discharge (NRD), length of stay (LOS), extended LOS, reoperation, mortality, all medical complications (individual analyses for cardiovascular, deep venous thrombosis/pulmonary embolism, genitourinary, neurological, respiratory, and systemic infection complications), and wound-related complications. For each outcome, two subanalyses were performed with studies that used either center-based (single- or multi-institution) or high-volume (national or state-wide) databases. RESULTS: Across 124 included studies, male patients had an increased incidence of mortality (OR 0.54, p < 0.0001) and all medical complications (OR 0.80, p = 0.0114), specifically cardiovascular (OR 0.68, p < 0.0001) and respiratory (OR 0.76, p = 0.0008) complications. Female patients were more likely to experience a wound-related surgical complication (OR 1.16, p = 0.0183). These findings persisted in the high-volume database subanalyses. Only center-based subanalyses showed that female patients were at greater odds of experiencing an NRD (OR 1.18, p = 0.0476), longer LOS (SMD 0.23, p = 0.0036), and extended LOS (OR 1.28, p < 0.0001). CONCLUSIONS: Males are more likely to experience death and medical complications, whereas females were more likely to face wound-related surgical complications. At the institution level, females more often experience NRD and longer hospital stays. These findings may better inform preoperative expectation management and provide more detailed postoperative risk assessments based on the patient's gender.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".