Constructing maternal morbidity – towards a standard tool to measure and monitor maternal health beyond mortality
Bibliographic record
Abstract
BACKGROUND: Maternal morbidity is a complex entity and its presentation and severity are on a spectrum. This paper describes the conceptualization and development of a definition for maternal morbidity, and the framework for its measurement: the maternal morbidity matrix, which is the foundation for measuring maternal morbidity, thus, the assessment tool. DISCUSSION: We define maternal morbidity and associated disability as "any health condition attributed to and/or complicating pregnancy and childbirth that has a negative impact on the woman's wellbeing and/or functioning." A matrix of 121 conditions was generated through expert meetings, review of the International Classification of Diseases and related health problems (ICD-10), literature reviews, applying the definition of maternal morbidity and a cut-off of >0.1% prevalence. This matrix has three dimensions: identified morbidity category, reported functioning impact and maternal history. The identification criteria for morbidity include 58 symptoms, 29 signs, 44 investigations and 35 management strategies; these criteria are aimed at recognizing the medical condition, or the functional impact/disability component that will capture the negative impact experienced by the woman. The maternal morbidity matrix is a practical framework for assessing maternal morbidity beyond near-miss. In light of the emerging attention to Universal Health Coverage (UHC) as part of the post-2015 Sustainable Development Goals (SDGs) planning, a definition and standard identification criteria are essential to measuring its extent and impact.
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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 | 0.061 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".