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Record W2050719637 · doi:10.1515/jpm-2012-0057

Vaginal progesterone to prevent preterm birth in multiple pregnancy: a randomized controlled trial

2012· article· en· W2050719637 on OpenAlexaff
Stephen Wood, Sue Ross, Selphee Tang, Leslie Miller, Reg Sauvé, Rollin Brant

Bibliographic record

VenueJournal of Perinatal Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicPreterm Birth and Chorioamnionitis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePregnancyRandomized controlled trialGestationObstetricsPlaceboGestational ageAdverse effectConfidence intervalPremature birthGynecologyInternal medicine

Abstract

fetched live from OpenAlex

AIM: We sought to assess the effect of progesterone in increasing or decreasing duration of pregnancy for mothers with twins and triplets. METHODS: Women with multiple pregnancy were randomized at 16+0 to 20+6 weeks' gestation to receive daily doses of either 90 mg progesterone vaginal gel or placebo until 35+6 weeks or delivery. Primary outcome was gestational age (GA) at delivery. Secondary outcomes included preterm birth, infant morbidity and mortality, and adverse events up to 28 days after delivery. A sample size of 40 per group was required to detect a 2-week difference in GA at delivery. RESULTS: Eighty-four women were randomized (42 to progesterone, 42 to placebo). Median GA at delivery was 36+3 and 36+2, respectively (difference, 1 day; 95% confidence interval, 4 to -1 days, P=0.585). There were no clinically or statistically significant differences between groups for risk of delivery before 35 or 37 weeks' GA, rates of infant morbidity and mortality, treatment compliance, or adverse events. CONCLUSIONS: Daily treatment with progesterone gel did not prolong multiple pregnancy compared with placebo. Effective prevention strategies are needed to reduce preterm deliveries among women with multiple gestations.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.293
Teacher spread0.275 · 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 designRandomized trial
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

Citations59
Published2012
Admission routes1
Has abstractyes

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