Unsuccessful misoprostol induction in pregnant women: an integrative review
Bibliographic record
Abstract
Objective: To analyze the evidence available in literature regarding unsuccessful labor induction with misoprostol in full-term pregnancies. Methods: This is an integrative review, carried out between January and November 2022, whose research question and descriptors were outlined using the PECO strategy. The searches were carried out in the MEDLINE, Web of Science, CINAHL, EMBASE and Scopus databases by two researchers independently as well as assessment. For the study selection and identification phase, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used. The risk of bias assessment of included articles was carried out using the Newcastle-Ottawa Scale. Results: A total of 3,674 articles were identified, and 84 were read in full, of which 11 comprised the review (n=9,010 pregnant women), published between 2005 and 2021, with the majority in the United States. Regarding the level of evidence, all articles were classified as 2b, assessed according to the design of each study. The study showed evidence regarding the following factors: High BMI (greater than 30 kg/m2), nulliparity, immature bishop, cervical length (greater than 30 mm), height, ethnicity (non-Caucasians from southern Europe) and fetal weight (greater equal to 4 kg). Conclusion: The objective study was achieved, having demonstrated six maternal factors and one fetal factor that can lead to unsuccessful induction. It is worth highlighting the need for evidence that incorporates the individuality of each characteristic and the contribution of this study to support the choice of the best conduct for each pregnancy on an individual basis stands out.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".