Core outcome sets for prevention and treatment of postpartum haemorrhage: an international Delphi consensus study
Bibliographic record
Abstract
OBJECTIVE: To develop core outcome sets (COS) for studies evaluating interventions for (1) prevention and (2) treatment of postpartum haemorrhage (PPH), and recommendations on how to report the COS. DESIGN: A two-round Delphi survey and face-to-face meeting. POPULATION: Healthcare professionals and women's representatives. METHODS: Outcomes were identified from systematic reviews of PPH studies and stakeholder consultation. Participants scored each outcome in the Delphi on a Likert scale between 1 (not important) and 9 (critically important). Results were discussed at the face-to-face meeting to agree the final COS. Consensus at the meeting was defined as ≥ 70% of participants scoring the outcome as critically important (7-9). Lectures, discussion and voting were used to agree how to report COS outcomes. MAIN OUTCOME MEASURES: Outcomes from systematic reviews and consultations. RESULTS: Both Delphi rounds were completed by 152/205 (74%) participants for prevention and 143/197 (73%) for treatment. For prevention of PPH, nine core outcomes were selected: blood loss, shock, maternal death, use of additional uterotonics, blood transfusion, transfer for higher level of care, women's sense of wellbeing, acceptability and satisfaction with the intervention, breastfeeding, and adverse effects. For treatment of PPH, 12 core outcomes were selected: blood loss, shock, coagulopathy, hysterectomy, organ dysfunction, maternal death, blood transfusion, use of additional haemostatic intervention, transfer for higher level of care, women's sense of wellbeing, acceptability and satisfaction with the intervention, breastfeeding, and adverse effects. Recommendations were developed on how to report these outcomes where possible. CONCLUSIONS: These COS will help standardise outcome reporting in PPH trials. TWEETABLE ABSTRACT: Core outcome sets for PPH: nine core outcomes for PPH prevention and 12 core outcomes for PPH treatment.
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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 | 0.376 | 0.317 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".