International virtual confidential reviews of infection-related maternal deaths and near-miss in 11 low- and middle-income countries – case report series and suggested actions
Bibliographic record
Abstract
Obstetric infections are the third most common cause of maternal mortality, with the largest burden in low and middle-income countries (LMICs). We analyzed causes of infection-related maternal deaths and near-miss identified contributing factors and generated suggested actions for quality of care improvement.An international, virtual confidential enquiry was conducted for maternal deaths and near-miss cases that occurred in 15 health facilities in 11 LMICs reporting at least one death within the GLOSS study. Facility medical records and local review committee documents containing information on maternal characteristics, timing and chain of events, case management, outcomes, and facility characteristics were summarized into a case report for each woman and reviewed by an international external review committee. Modifiable factors were identified and suggested actions were organized using the three delays framework.Thirteen infection-related maternal deaths and 19 near-miss cases were reviewed in 20 virtual meetings by an international external review committee. Of 151 modifiable factors identified during the review, delays in receiving care contributed to 71/85 modifiable factors in maternal deaths and 55/66 modifiable factors in near-miss cases. Delays in reaching a GLOSS facility contributed to 5/85 and 1/66 modifiable factors for maternal deaths and near-miss cases, respectively. Two modifiable factors in maternal deaths were related to delays in the decision to seek care compared to three modifiable factors in near-miss cases. Suboptimal use of antibiotics, missing microbiological culture and other laboratory results, incorrect working diagnosis, and infrequent monitoring during admission were the main contributors to care delays among both maternal deaths and near-miss cases. Local facility audits were conducted for 2/13 maternal deaths and 0/19 near-miss cases. Based on the review findings, the external review committee recommended actions to improve the prevention and management of maternal infections.Prompt recognition and treatment of the infection remain critical addressable gaps in the provision of high-quality care to prevent and manage infection-related severe maternal outcomes in LMICs. Poor uptake of maternal death and near-miss reviews suggests missed learning opportunities by facility teams. Virtual platforms offer a feasible solution to improve routine adoption of confidential maternal death and near-miss reviews locally.
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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 | 0.043 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".