Frequency of Success Rate of Cervical Cerclage in Preventing Preterm Deliveries
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".