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Record W1983502224 · doi:10.4021/jcgo.v2i2.143

Prophylactic Cervical Cerclage (Modified Shirodkar Operation) for Twin and Triplet Pregnancies After Fertility Treatment

2013· article· en· W1983502224 on OpenAlexvenueno aff
Leonidas Mamas, Eudoxia Mamas

Bibliographic record

VenueJournal of Clinical Gynecology and Obstetrics · 2013
Typearticle
Languageen
FieldMedicine
TopicPreterm Birth and Chorioamnionitis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObstetricsFertilityGestational ageCervical cerclageTwin PregnancyGestationPregnancyNeonatal intensive care unitGynecologyCervixPopulationPediatricsCancer

Abstract

fetched live from OpenAlex

Background: Multiple pregnancy rates have increased in the recent years mainly due to an increase in the number of assisted reproduction cycles performed. These pregnancies are associated with maternal and perinatal complications. These complications may arise due to premature delivery. This clinical study was performed in order to assess the effectiveness of prophylactic vaginal cervical cerclage in multiple pregnancies. Methods: A modified Shirodkar method was applied in twin and triplet pregnancies conceived by fertility treatments, at 13 to 14 weeks of gestation. The suture was fully embedded under the vaginal mucosa in order to avoid the risk of infection. Results: Our cohort of patients included 31 women with twin and five with triplet pregnancies. The mean gestational age for twins and triplets was 35 + 4 and 33 + 6 and the mean birthweight was 2,267 g and 1,820 g, respectively. Nearly half of the twins and all of the triplets delivered were admitted in the neonatal intensive care unit. Only one neonate had a very low birth weight. No significant differences were noted in the effect of different fertility techniques in the outcome measures studied. Conclusions: Multiple pregnancies conceived with the aid of fertility treatment benefited from the prophylactic application of a prophylactic cervical vaginal cerclage. doi: http://dx.doi.org/10.4021/jcgo143w

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)0.000
metaresearch head score (Gemma)0.008
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 score0.329
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.008
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 head0.052
GPT teacher head0.344
Teacher spread0.292 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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