Severe Maternal Morbidity: Fundamental Concepts
Bibliographic record
Abstract
Despite the importance of severe maternal morbidity (SMM) as a medical concern, there is a lack of consensus on several issues related to this topic. This article reviews fundamental concepts associated with SMM, and provides a historical and scientific perspective on these critical issues. SMM is defined as the population rate of serious illnesses in pregnancy, childbirth, or the puerperium. The SMM rate depends on the component severe maternal illnesses included in composite SMM, the rigor with which these components are defined, and the data sources used for surveillance (among others). These issues pose a serious challenge for spatiotemporal comparisons of SMM, especially for between-country comparisons of composite SMM rates. The different severe maternal illnesses included within composite SMM display substantial heterogeneity in terms of frequency, clinical burden of illness, and population impact. Other concerns include the need to address SMM in early pregnancy hospitalizations and postpartum readmissions; the need for nuanced interpretation of adjusted rates; and whether assigning a singular underlying severe illness is preferable to assigning one or more severe illnesses for each woman. Finally, the heterogeneity of the composite measure warrants careful consideration of the need for an all-inclusive composite outcome versus a more restricted/specific outcome depending on the study question or surveillance priority. Prevention programs addressing SMM need to focus on component illnesses and identify opportunities for intervention based on case reviews or epidemiologic analyses of risk factors for the specific illness. There is a lack of consensus on several concepts related to SMM, and this calls for a careful consideration of the clinical and epidemiologic issues related to quantifying and interpreting SMM rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".