Physician procedure volume and related adverse events after surgically induced abortion: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Induced abortion is a common procedure performed by physicians with varying degrees of clinical experience. We aimed to determine whether a physician's procedure volume influences complications after induced abortion. METHODS: We obtained population-based retrospective data on surgically induced abortion procedures in Ontario between 2003 and 2015 from Ontario health administrative databases held at ICES. Physician procedure volume was defined as the number of surgically induced abortions performed in the 1-year period preceding the index procedure date, categorized as low (< 10th percentile of yearly volume) or higher (≥ 10th percentile). The primary outcome was a severe adverse event (maternal end organ damage, severe maternal morbidity, intensive care unit admission or death) within 42 days after an induced abortion. The secondary outcome was any adverse event within 42 days. RESULTS: Among 529 141 surgical abortion procedures, we found 850 severe adverse events (1.6 per 1000 procedures, 95% confidence interval [CI] 1.5-1.7), and 5664 any adverse events (10.7 per 1000 procedures, 95% CI 10.4-11.0). Severe adverse events occurred in 194 out of 52 889 procedures in the low-volume group (3.7 per 1000 procedures, 95% CI 3.2-4.2) compared with 656 out of 476 252 procedures in the higher-volume group (1.4 per 1000 procedures, 95% CI 1.3-1.5), an adjusted odds ratio (OR) of 1.91 (95% CI 1.41-2.59). The odds of any adverse event were also higher in the low-volume versus higher-volume group (adjusted OR 1.19, 95% CI 1.02-1.40). INTERPRETATION: Low physician procedure volumes are associated with an elevated risk of a complication after surgically induced abortion. Future investigation should compare processes of care between low- and higher-volume physicians to facilitate quality improvement in abortion care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".