Family physician and obstetrician episiotomy rates in low-risk obstetrics in southern Alberta.
Bibliographic record
Abstract
OBJECTIVE: To examine the episiotomy rate for women delivering in a regional hospital versus the rate in rural hospitals. DESIGN: Retrospective review of low-risk delivery charts for a 12-month period (2006 to 2007). SETTING: One regional and 3 rural hospitals in southern Alberta. PARTICIPANTS: Charts were reviewed for a random sample of 10% of the women with low-risk deliveries at the regional hospital, and all such women at the participating rural hospitals. Eligible women were nulliparous or multiparous, were at 37 or more weeks' gestation, and delivered live newborns vaginally, including spontaneous and assisted vaginal deliveries. Low-risk deliveries were defined by the absence of high-risk maternal, prenatal, and perinatal features. MAIN OUTCOME MEASURES: Details of the delivery, including use of episiotomy. RESULTS: Charts were reviewed for 115 women who delivered in the regional hospital and for 140 women from the rural hospitals. Maternal and infant characteristics did not differ between settings (mean age 26 years, median parity 1, mean birth weight 3433 g [regional] and 3462 g [rural], and mean head circumference 35 cm). Episiotomies were performed in 13% of regional and 4% of rural deliveries (P = .01). Perineal tears occurred in 65% of regional (3 with third- to fourth-degree tears) and 57% of rural (2 with third- to fourth-degree tears) deliveries (P = .20). Deliveries were carried out by 12 FPs and 6 obstetricians in the regional centre, and by 19 FPs in the rural hospitals. CONCLUSION: In our study, both rural and regional practitioners in southern Alberta demonstrated a "restrictive" use of episiotomy, in keeping with current evidence-based guidelines. Further prospective research is needed to examine how physician, maternal, and pregnancy characteristics affect episiotomy and perineal tear rates.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".