Atonic Postpartum Hemorrhage: Blood Loss, Risk Factors, and Third Stage Management
Bibliographic record
Abstract
OBJECTIVE: Atonic postpartum hemorrhage rates have increased in many industrialized countries in recent years. We examined the blood loss, risk factors, and management of the third stage of labour associated with atonic postpartum hemorrhage. METHODS: We carried out a case-control study of patients in eight tertiary care hospitals in Canada between January 2011 and December 2013. Cases were defined as women with a diagnosis of atonic postpartum hemorrhage, and controls (without postpartum hemorrhage) were matched with cases by hospital and date of delivery. Estimated blood loss, risk factors, and management of the third stage labour were compared between cases and controls. Conditional logistic regression was used to adjust for confounding. RESULTS: The study included 383 cases and 383 controls. Cases had significantly higher mean estimated blood loss than controls. However, 16.7% of cases who delivered vaginally and 34.1% of cases who delivered by Caesarean section (CS) had a blood loss of < 500 mL and < 1000 mL, respectively; 8.2% of controls who delivered vaginally and 6.7% of controls who delivered by CS had blood loss consistent with a diagnosis of postpartum hemorrhage. Factors associated with atonic postpartum hemorrhage included known protective factors (e.g., delivery by CS) and risk factors (e.g., nulliparity, vaginal birth after CS). Uterotonic use was more common in cases than in controls (97.6% vs. 92.9%, P < 0.001). Delayed cord clamping was only used among those who delivered vaginally (7.7% cases vs. 14.6% controls, P = 0.06). CONCLUSION: There is substantial misclassification in the diagnosis of atonic postpartum hemorrhage, and this could potentially explain the observed temporal increase in postpartum hemorrhage 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 | 0.001 | 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".