Should case management be considered a component of obstetrical interventions for pregnancies at risk of preterm birth?
Bibliographic record
Abstract
Preterm birth remains the leading cause of morbidity and mortality among nonanomalous neonates in the United States. Unfortunately, preterm birth rates remain high despite current medical interventions such as progestogen supplementation and cerclage placement. Case management, which encompasses coordinated care aimed at providing a more comprehensive and supportive environment, is a key component in improving health and reducing costs in other areas of medicine. However, it has not made its way into the general lexicon and practice of obstetrical care. Case management intended for decreasing prematurity or ameliorating its consequences may include specialty clinics, social services, coordination of specialty services such as nutrition counseling, home visits or frequent phone calls by specially trained personnel, and other elements described herein. It is not currently included in nor is it advocated for as a recommended prematurity prevention approach in the American College of Obstetricians and Gynecologists or Society for Maternal-Fetal Medicine guidelines for medically indicated or spontaneous preterm birth prevention. Our review of existing evidence finds consistent reductions or trends toward reductions in preterm birth with case management, particularly among individuals with high a priori risk of preterm birth across systematic reviews, metaanalyses, and randomized controlled studies. These findings suggest that case management has substantial potential to improve the environmental, behavioral, social, and psychological factors with patients at risk of preterm birth.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".