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Record W2964376867 · doi:10.1515/jpm-2019-0153

Standard and adjusted criteria for the use of the misoprostol vaginal insert for labor induction: a comparative cohort study

2019· article· en· W2964376867 on OpenAlexaff
Aleke Brandstetter, Moritz Döbert, Peter Schwaerzler, Timm F. Döbert, Hendrik Hasselbeck, Wolfgang Henrich

Bibliographic record

VenueJournal of Perinatal Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsUniversity of Alberta
FundersPfizer
KeywordsMedicineMisoprostolLabor inductionInduction of laborObstetricsCohortCohort studyInsert (composites)GynecologyPregnancyOxytocinInternal medicineAbortion

Abstract

fetched live from OpenAlex

Objective To compare the efficacy of misoprostol vaginal insert (MVI) for labor induction using standard and adjusted criteria. Methods A single-center, comparative cohort study using a consecutive series of pregnant women ≥37/0 weeks undergoing labor induction with either standard criteria for MVI (administration for up to 24 h; MVI-24) or with adjusted criteria (MVI administration for a maximum of 10 h; MVI-10) conducted at a tertiary academic center in Germany. The primary outcomes were the time from start of induction to any delivery and cesarean delivery rate. Results A total of 138 women were included in the study, 69 in each group. The mean time from MVI administration to any delivery showed no significant difference between the MVI-24 and MVI-10 groups (954 vs. 969 min, respectively; P = 0.679). The cesarean delivery rate was proportionally lower for the MVI-10 group [39.1% (27/69) vs. 24.6% (17/69); P = 0.10]. Conclusion The time from induction to delivery with MVI was similar when using standard criteria of up to 24 h of exposure vs. adjusted criteria of up to 10 h of exposure. Although the threshold for statistical significance for cesarean section was not attained, there is nonetheless a considerable difference between the MVI-24 and MVI-10 groups.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.137
GPT teacher head0.418
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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