The Conceptualization of Pregnancy-Associated Maternal Morbidity in Research Studies: A Scoping Review [ID: 1376717]
Bibliographic record
Abstract
INTRODUCTION: This study aimed to review the clinical literature to understand how maternal morbidity has been conceptualized. METHODS: We conducted a scoping review wherein we searched four databases until May 2022 for studies on maternal morbidity. We screened abstracts in duplicate and extracted data on the study scope, terms used to describe maternal morbidity, and conditions included. Results were presented descriptively. RESULTS: We identified 8,178 titles, of which 589 studies from 1986 to 2022 were included in the final analysis. Included studies used 73 terms to describe maternal morbidity. While 217 (37%) studies described maternal morbidity in the context of the general pregnant population, others described more specific contexts, for example, hypertensive disorders of pregnancy (43 studies, 7%), obesity (38 studies, 6%), and gestational and pregestational diabetes (31 studies, 5%). In terms of component outcomes, 156 (26%) studies utilized the World Health Organization criteria for maternal near miss and potentially life-threatening conditions or the Centers for Disease Control and Prevention criteria for severe maternal morbidity, while others included between one and 45 outcomes, the most frequent of which were intensive care unit admission, preeclampsia, eclampsia, hemorrhage, uterine rupture, hysterectomy, and blood transfusion. Notably, 36 (6%) studies included fetal or neonatal outcomes when describing maternal morbidity. CONCLUSION: Although maternal morbidity is reported in a large number of pregnancy studies, there is no consistency in what outcomes are included in its definition. There is need for a standardized definition of maternal morbidity that embodies the perspectives of persons with lived experience of pregnancy.
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 | 0.097 | 0.289 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.044 | 0.045 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".