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Record W2083243521 · doi:10.3109/14767058.2014.900038

Cervical length in asymptomatic twin pregnancies: prospective multicenter comparison of predictive indicators

2014· article· en· W2083243521 on OpenAlexaff
Christine Lévêque, Christophe Vayssière, R. Favre, François Audibert, Marie Pierre Chauvet, Françoise Maillard, Virginie Elhinger, Catherine Arnaud

Bibliographic record

VenueThe Journal of Maternal-Fetal & Neonatal Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicPreterm Birth and Chorioamnionitis
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de Montréal
Fundersnot available
KeywordsAsymptomaticMedicineCutoffProspective cohort studyPredictive valuePredictive value of testsObstetricsReceiver operating characteristicPositive predicative valueArea under the curveGynecologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

UNLABELLED: Abstract Objective: To determine whether cervical shortening between 22 and 27 weeks predicts spontaneous preterm delivery before 34 weeks better than a single cervical length (CL) measurement at 22 or 27 weeks in asymptomatic twins. METHODS: Prospective 13-center study over a 2-year-period. CL was measured in 120 asymptomatic twin pregnancies. The area under the ROC curve was calculated for each parameter and the cutoff point providing the best diagnostic performance, sensitivity and specificity for predicting spontaneous delivery<34 weeks was defined too. RESULTS: About 13/116 women gave birth before 34 weeks. The three ROC curves differed significantly at the 0.05 level. The best cutoff points were CL≤35 mm at 22 weeks, CL≤25 mm at 27 weeks and cervical shortening≥20%. For equal sensitivity values for each, specificity for CL≤25 mm at 27 weeks was 87.5%, significantly better. CONCLUSIONS: The performance of cervical shortening for the prediction of preterm delivery of asymptomatic twins before 34 weeks does not differ from that of CL measurements at 22 or 27 weeks. The modest predictive value of CL at 22 weeks and of cervical shortening is an argument against recommending routine CL measurements.

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)0.001
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 score0.029
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.271
Teacher spread0.263 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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