Prediction and prevention of spontaneous preterm birth
Bibliographic record
Abstract
Incidence of preterm delivery ranges from 7-12% of all gestations and premature birth is one of the main causes for newborn morbimortality. It is responsible for over three quarters of neonatal deaths, minus congenital malformations. Several strategies can be adopted to reduce premature delivery rates, including risk factor identification and prophylactic use of progesterone. Among the main actions of progesterone is its relaxing effect upon uterine muscles, the ability to block the effects of cytokin, and its antiinflammatory and immunosuppresive effects. The use of exogenous progesterone reduces the rates of prematurity for patients under risk of premature delivery, such as those with a history of premature deliveries, and short cervix as revealed by transvaginal ultrasound in the second quarter of pregnancy. This review aims to highlight important aspects to be considered in the outpatient clinic and describe the main predictive and preventive actions of premature birth available in obstetric care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".