Cross-sectional study evaluating the effectiveness of the Mozambique–Canada maternal health project abstraction tool for maternal near miss identification in Inhambane province, Mozambique
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study are to determine whether the additional clinical criteria of the Mozambique maternal near miss abstraction tool enhance the effectiveness of the original WHO abstraction tool in identifying maternal near miss cases and also evaluate the impact of sociodemographic factors on maternal near miss identification. DESIGN: Cross-sectional study. SETTING: Two secondary referral hospitals in Inhambane province, Mozambique from 2021 to 2022. PARTICIPANTS: From August 2021 to February 2022, 2057 women presenting at two hospitals in Inhambane Province, Mozambique, were consecutively enrolled. Eligible participants included women admitted during pregnancy, labour, delivery, or up to 42 days post partum. Selection criteria focused on women experiencing obstetric complications, while those without complications or with incomplete medical records were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was identifying maternal near miss cases using the original WHO Disease criterion and the additional clinical criteria from the Mozambique-Canada Maternal Health Project abstraction tool. Secondary outcomes included the association between sociodemographic factors and maternal near miss identification. All outcomes were measured as planned in the study protocol. RESULTS: The new Mozambique-Canada abstraction tool identified more maternal near miss cases (28.2% for expanded disease and 21.1% for comorbidities) compared with the original WHO tool (16.2%). Hypertension and anaemia from the newer criteria were strongly associated with the original WHO Disease criterion (p<0.001), with kappa values of 0.58 (95% CI 0.53 to 0.63) and 0.21 (95% CI 0.16 to 0.26), respectively. Distance to health facilities was significantly associated, with women living over 8 km away having higher odds (OR=2.47, 95% CI 1.92 to 3.18, p<0.001). Type of hospital also influenced identification, with lower odds at Vilankulo Rural Hospital for Expanded Disease criterion (OR=0.70, 95% CI 0.57 to 0.87, p=0.001), but higher odds for comorbidities criterion (OR=3.13, 95% CI 2.40 to 4.08, p<0.001). Finally, older age was associated with higher odds of identification under the comorbidities criterion, particularly for women aged 30-39 (OR=3.06, 95% CI 2.15 to 4.36) as well as those 40 years or older (OR=4.73, 95% CI 2.43 to 9.20, p<0.001). CONCLUSIONS: The Mozambique-Canada Maternal Health Project tool enhances maternal near miss identification over the original WHO tool by incorporating expanded clinical criteria, particularly for conditions like hypertension and anaemia. Sociodemographic factors, including healthcare access, hospital type and maternal age, significantly impact near miss detection. These findings support integrating the expanded criteria into the WHO tool for improved identification of maternal near misses in Mozambique and similar low-resource settings. Future research should examine the tool's effectiveness across varied healthcare contexts and populations.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".