Assessing the quality of record keeping for cesarean deliveries: results from a multicenter retrospective record review in five low-income countries
Bibliographic record
Abstract
BACKGROUND: Reliable, timely information is the foundation of decision making for functioning health systems; the quality of decision making rests on quality data. Routine monitoring, reporting, and review of cesarean section (CS) indications, decision-to-delivery intervals, and partograph use are important elements of quality improvement for maternity services. METHODS: In 2009 and 2010, a sample of CS records from calendar year 2008 was reviewed at nine facilities in Bangladesh, Guinea, Mali, Niger, and Uganda. Data from patient records and hospital registers were collected on key aspects of care such as timing of key events, indications, partograph use, maternal and fetal outcomes. Qualitative interviews were conducted with key informants at all study sites to provide contextual background about CS services and record keeping practices. RESULTS: A total of 2,941 records were reviewed and 57 key informant interviews were conducted. Patient record-keeping systems were of varying quality across study sites: at five sites, more than 20% of records could not be located. Across all sites, patient files were missing key aspects of CS care: timing of key events (e.g., examination, decision to perform CS), administration of prophylactic antibiotics, maternal complications, and maternal and fetal outcomes. Rates of partograph use were low at six sites: 0 to 23.9% of patient files at these sites had a completed partograph on file, and among those found, 2.1% to 65.1% were completed correctly. Information on fetal outcomes was missing in up to 40% of patient files. CONCLUSIONS: Deficits in the quality of CS patient records across a broad range of health facilities in low-resource settings in four sub-Saharan Africa countries and Bangladesh indicate an urgent need to improve record keeping.
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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 | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".