Applicability of the WHO Maternal Near Miss Criteria in a Low-Resource Setting
Bibliographic record
Abstract
BACKGROUND: Maternal near misses are increasingly used to study quality of obstetric care. Inclusion criteria for the identification of near misses are diverse and studies not comparable. WHO developed universal near miss inclusion criteria in 2009 and these criteria have been validated in Brazil and Canada. OBJECTIVES: To validate and refine the WHO near miss criteria in a low-resource setting. METHODS: A prospective cross-sectional study was performed in a rural referral hospital in Tanzania. From November 2009 until November 2011, all cases of maternal death (MD) and maternal near miss (MNM) were included. For identification of MNM, a local modification of the WHO near miss criteria was used, because most laboratory-based and some management-based criteria could not be applied in this setting. Disease-based criteria were added as they reflect severe maternal morbidity. In the absence of a gold standard for identification of MNM, the clinical WHO criteria were validated for identification of MD. RESULTS: 32 MD and 216 MNM were identified using the locally adapted near miss criteria; case fatality rate (CFR) was 12.9%. WHO near miss criteria identified only 60 MNM (CFR 35.6%). All clinical criteria, 25% of the laboratory-based criteria and 50% of the management-based criteria could be applied. The threshold of five units of blood for identification of MNM led to underreporting of MNM. Clinical criteria showed specificity of 99.5% (95%CI: 99.4%-99.7%) and sensitivity of 100% (95%CI: 91.1%-100%). Some inclusion criteria did not contribute to the identification of cases and therefore may be eligible for removal. CONCLUSION: The applicability of the WHO near miss criteria depends on the local context, e.g. level of health care. The clinical criteria showed good validity. Lowering the threshold for blood transfusion from five to two units in settings without blood bank and addition of disease-based criteria in low-resource settings is recommended.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".