Postpartum haemorrhage in high‐resource settings: Variations in clinical management and future research directions based on a comparative study of national guidelines
Bibliographic record
Abstract
OBJECTIVE: To compare guidelines from eight high-income countries on prevention and management of postpartum haemorrhage (PPH), with a particular focus on severe PPH. DESIGN: Comparative study. SETTING: High-resource countries. POPULATION: Women with PPH. METHODS: Systematic comparison of guidance on PPH from eight high-income countries. MAIN OUTCOME MEASURES: Definition of PPH, prophylactic management, measurement of blood loss, initial PPH-management, second-line uterotonics, non-pharmacological management, resuscitation/transfusion management, organisation of care, quality/methodological rigour. CONCLUSIONS: Our study highlights areas where strong evidence is lacking. There is need for a universal definition of (severe) PPH. Consensus is required on how and when to quantify blood loss to identify PPH promptly. Future research may focus on timing and sequence of second-line uterotonics and non-pharmacological interventions and how these impact maternal outcome. Until more data are available, different transfusion strategies will be applied. The use of clear transfusion-protocols are nonetheless recommended to reduce delays in initiation. There is a need for a collaborative effort to develop standardised, evidence-based PPH guidelines. RESULTS: Definitions of (severe) PPH varied as to the applied cut-off of blood loss and incorporation of clinical parameters. Dose and mode of administration of prophylactic uterotonics and methods of blood loss measurement were heterogeneous. Recommendations on second-line uterotonics differed as to type and dose. Obstetric management diverged particularly regarding procedures for uterine atony. Recommendations on transfusion approaches varied with different thresholds for blood transfusion and supplementation of haemostatic agents. Quality of guidelines varied considerably.
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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 | 0.097 | 0.248 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".