Evaluating the Effectiveness of the Mozambique-Canada Maternal Health (MCMH) Project Abstraction Tool in the Identification of Maternal Near-Miss (MNM) Events
Bibliographic record
Abstract
Abstract Maternal Near-Miss (MNM) is described as a woman who survives a severe obstetric event. The World Health Organization (WHO) developed an abstraction tool in 2009 for identifying MNMs, but it has come under criticism for not being suitable for use in low-resource settings. The maternal near-miss tool developed by the Mozambique-Canada Maternal Health Project, including additional clinical criteria, is an adaptation of the WHO version to suit the resource availability in Mozambique. This study examined whether these additional criteria enhanced maternal near-miss identification; if so, whether this was observed in particular groups of women. A cross-sectional study was conducted in two hospitals, the Provincial Hospital of Inhambane province, a tertiary referral care center, and a rural hospital, Vilankulo Rural Hospital, with a large rural catchment area (approximately 46,543 inhabitants), in the Inhambane province in Mozambique. Consecutive admissions in the maternity wards in these two hospitals between August 2021 and February 2022 were eligible and data from 2057 women were included. Chi-square test of independence, kappa statistics, and multiple logistic regression analyses were performed to address the study aims. The newer tool with additional clinical criteria identified more maternal near-misses (Expanded Disease criterion = 28.2%; Comorbidities criterion = 21.1%) than the original WHO tool (16.20%). Hypertension and Anemia, two criteria in the newer tool, showed strong associations with the original WHO disease criterion (p < 0.001). Hypertension demonstrated a moderate agreement with the WHO disease criterion (κ = 0.58, 95% CI: 0.53-0.63) while anemia showed a fair agreement (κ = 0.21, 95% CI: 0.16-0.26). However, HIV/AIDS, the most prevalent comorbidity, was not significantly associated with the original WHO disease criterion. Furthermore, socio-demographic indicators like distance from home to hospital, age of woman, and type of health facility (provincial or rural district) were significant predictors of identifying maternal near-misses. In conclusion, incorporating additional criteria enhances – it casts a larger net – the original WHO disease criterion’s capacity to identify maternal near-misses. Distance from home to the hospital and age emerge as strong predictors for recognizing MNMs in Inhambane province.
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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 | 0.036 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".