Bibliographic record
Abstract
IntroductionWith declining maternal mortality rates in high income countries (HICs), severe maternal morbidity (SMM) is becoming an important quality measure of maternal care. However, there is no international consensus on the definition and types of SMM. This study aims to critically analyze published literature on SMM in HICs. ObjectivesTo compare definitions and criteria used to identify SMM, and to identify the main types and risk factors contributing to SMM in eight HICs. MethodsThree databases were searched, results were filtered, and ten studies were critically appraised. ResultsSix of the articles discussed SMM identification criteria and proposed definition modifications. Longer hospital stay and admission to intensive care unit were suggested as additional criteria. Disease-based criteria was shown to be superior to organ dysfunction criteria. Seven articles detailed common types of SMM as severe haemorrhage, hypertensive disorders, and pre-eclampsia/eclampsia. Six articles described SMM risk factors, of which advanced maternal age and caesarean delivery were most common. DiscussionThis literature review identified disease-based criteria and Canadian study criteria as promising measures of SMM. It also identified several types and risk factors of SMM common between HICs. These findings can help physicians identify women at risk of SMM. The study is however limited to eight HICs and ten studies. Further research should aim to investigate how the measures compare with previous sources of criteria, and to discern the association of weight and race risk factors with SMM.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".