Who is at risk? The development of a tool to predict and prevent postpartum hemorrhage
Bibliographic record
Abstract
Background: Postpartum hemorrhage remains the main cause of maternal mortality in Low and Middle Income Countries. There is a need to advocate for extra vigilance to recognize women at a greater risk and implement early intervention for Postpartum hemorrhage prevention. The purpose of the present study is to develop a content validated risk assessment tool for the prediction and prevention of Postpartum hemorrhage among childbearing women.Methods: This study is drawn from a larger mixed method sequential exploratory study. Factors influencing the prevention of Postpartum hemorrhage were identified from a scoping review and qualitative descriptive studies previously conducted. To establish content validity Index of the instrument, content experts assessed each item of the tool for comprehensiveness, relevance, and face validity. The tool was pilot tested to assess its clinical utility by fifteen (15) health care providers purposively selected from one district hospital based on a minimum of one-year experience in maternity. Ethical considerations were observed.Results: The Risk Assessment Tool went through three rounds of assessment for its content validity. The final round of quantification of the content validity demonstrates that 4 items out of 46 had an Item Content Validity Index (I-CVI) of 0.85 while 42 had the maximum I-CVI of 1. The overall Scale Content Validity Index/ Average (S-CVI/Ave) was 0.98, and the universal approach of Scale Content Validity Index/Universal Agreement (S-CVI/UA) was 0.91. The assessment of clinical utility of Risk Assessment Tool for the Prediction and Prevention of Postpartum hemorrhage among Childbearing women (RATP) demonstrates that its format allows easy recording of findings and using the tool can be an added value for prevention of PPH.Conclusions: The risk assessment tool for the prediction and prevention of Postpartum hemorrhage is intended to be used by health care providers in Rwanda to identify mothers at risk of developing PPH and implement timely prevention strategies. The clinical use of the tool can be vital in the development of accurate preventive approaches by key policy makers in Rwanda in particular and in other developing countries.
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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 | 0.035 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".