An evaluation of obstetrical data collection at health institutions in Mbarara Region, Uganda and Benue State, Nigeria
Bibliographic record
Abstract
Obstetrical decision-making, particularly for referrals, relies on data. Rates of completion for obstetrical data at health institutions in low-to-middle-income countries are not well documented. This assessment evaluated obstetrical data sources at health centers and hospitals in Mbarara Region, Uganda, and Benue State, Nigeria. We compared routinely collected obstetrical data to a proposed minimal dataset that was validated in Benue State: the Community Maternal Danger Score (CMDS). Overall, we found that the variables from Ugandan institutions were reflective of the scope of the CMDS, but had low completion rates. The variables recorded at Nigerian institutions were less comprehensive, but more often completed. Therefore, we recommend that obstetrical data collection be standardized. The CMDS can form the basis of a minimal dataset to reduce missingness for variables and promote effective risk assessment, as well as timely analysis and dissemination of obstetrical data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".