Development and Assessment of Indicators for Quality of Care in Severe Preeclampsia/Eclampsia and Postpartum Hemorrhage
Bibliographic record
Abstract
Severe preeclampsia/eclampsia and postpartum hemorrhage (PPH) are serious obstetric problems worldwide. Quality improvement of care measured by evidence-based indicators is recommended as a recent important strategy; however, the indicators for quality of care of these two conditions have not been established. This study aimed to develop such indicators and assess their validity, reliability, and feasibility at different contextual levels. Of 32 initially valid indicators for care of severe preeclampsia/eclampsia, after two rounds of Delphi technique, 21 and 30 indicators were agreed to be suitable to monitor care at district and referral hospitals. Of 13 initial indicators for PPH, 8 and 13 indicators were selected, respectively. The interrater reliability of indicators varied from 0.28 to 0.63. At least three-fourths of all indicators rated by local doctors and nurses were assessed as feasible in terms of relevance, measurability, and improvability. The process identified reliable and feasible performance indicators to monitor quality of care in severe preeclampsia/eclampsia and PPH for either basic or comprehensive emergency obstetric care (EmOC). The informative applicability of these indicators in clinical practice needs further evaluation.
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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 | 0.060 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".