Inter‐hospital variation in use of obstetrical blood transfusion: a population‐based cohort study
Bibliographic record
Abstract
OBJECTIVE: To identify the extent of hospital-to-hospital variation in use of obstetrical blood transfusion. DESIGN: Population-based cohort study linking provincial perinatal and blood transfusion registries. SETTING: British Columbia, Canada, 2004-2015. POPULATION: All pregnant women delivering at or beyond 20 weeks' gestation at any British Columbia hospital. METHODS: Mixed-effects regression models were used to estimate hospital-specific transfusion rates after sequentially accounting for (1) the role of random variation, (2) maternal medical and obstetrical characteristics (i.e. patient case mix) and (3) institutional and delivery factors (such as use of instrumental or caesarean delivery). MAIN OUTCOME MEASURES: Hospital-specific use of obstetrical red blood cell transfusion. RESULTS: Among 44 hospitals, crude institutional transfusion rates across the study period ranged from 3.7 to 23.6 per 1000, with an average of 8.3 per 1000. After adjusting for maternal characteristics, institution and delivery risk factors, a nearly three-fold difference in rates between the 10th and 90th percentile remained (5.4-14.5 per 1000). Twelve sites had rates significantly higher or lower than the provincial average. Women residing in remote areas were 2.5-fold (95% CI 1.8-3.5] more likely to receive a blood transfusion than were women residing in metropolitan areas. CONCLUSIONS: Meaningful variation between hospitals in use of blood transfusion during pregnancy was not explained by differences in patient case-mix or institutional factors, suggesting that over- or under-utilisation of this resource may be occurring in obstetrical care. TWEETABLE ABSTRACT: Use of blood transfusion in pregnant women varied broadly between hospitals in British Columbia, Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".