Constructing maternal morbidity – towards a standard tool to measure and monitor maternal health beyond mortality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it