Evaluating equity of access and predictors of minimally invasive hysterectomy for endometrial and cervical cancer from 2000 to 2017 in Ontario, Canada: A population‐based cohort study
Bibliographic record
Abstract
Abstract Introduction We sought to assess the uptake of minimally invasive hysterectomy among patients with endometrial and cervical cancer in Ontario, Canada, and assess the equity of access to minimally invasive surgery (MIS) by evaluating associations with patient, disease, institutional, and provider factors. Methods This is a retrospective population‐based cohort study of hysterectomy for endometrial and cervical cancer in Ontario (2000–2017). Surgical approach, clinicopathologic, sociodemographic, institutional, and provider factors were identified through administrative databases. Fisher's exact, χ2, Wilcoxon rank sum, logistic regression, and Cox proportional hazards modeling were used to explore factors associated with MIS. Results A total of 27 652 patients were included. In total, 6199/24 264 (26%) endometrial and 842/3388 (25%) cervical cancer patients received MIS. The proportion of MIS to open surgeries increased from <0.1% in 2000 to over 55% in 2017 (odds ratio [OR] = 1.31, confidence interval [CI] = 1.28–1.34). Low‐income quintile, rurality, low hospital volume, nonacademic hospital, nongynecologic oncology surgeon, and earlier year of surgeon graduation were associated with reduced odds of MIS (OR < 1). Conclusions The uptake of MIS hysterectomy increased steadily over the time period. Receipt of MIS is dependent upon multiple social determinants, provider variables, and systems factors. These disparities raise concern for health equity in Ontario and have significant implications for health systems planning and resource allocation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".