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Record W3212990272 · doi:10.53350/pjmhs211592830

Frequency of Success Rate of Cervical Cerclage in Preventing Preterm Deliveries

2021· article· en· W3212990272 on OpenAlexaff
Javeria Saleem, Nadia Pervaiz, Shama Naz, Tanzeela Hassan, Afshan Rani, Gul Afshan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPreterm Birth and Chorioamnionitis
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsMedicineCervical cerclageCervical insufficiencyObstetricsPreterm deliveryCervical dilatationPregnancyGestationCervix

Abstract

fetched live from OpenAlex

Objective: Determination of success rate of cervical cerclage in prevention of preterm deliveries in patients with cervical incompetence. Study Design: Case Series study. Place and Duration of Study: Study was conducted at Khyber Teaching Hospital for a period of six months from 29 March, 2018 to 29 September, 2018. Methodology: 97 pregnant women were recruited who had a history of previous miscarriages or pre-term delivery. Cervical Cerclage was performed on these patients who were then observed till delivery to ascertain the success rate of cervical cerclage in preventing pre-term deliveries in these patients. Results: In this study mean age was 30 years with SD 8.316. 63% patients were nulli para (with previous second trimester losses) and 37% patients were multi para (with previous pre-term deliveries). 78% delivered at term and 22% delivered preterm. 80% of babies delivered with good apgar score and weight greater than 2.5 kg where as 20% of babies delivered with low apgar score and weight less than 2.5kg. Overall success rate of cervical cerclage was 80%. Conclusion: Our study concluded that success rate of cervical cerclage was 80% in preventing pre-term deliveries in patients having cervical incompetence. Keywords: Cervical Cerclage, Pre-term deliveries, Cervical incompetence, Trans-vaginal

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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