CERVICAL PESSARY PLUS VAGINAL PROGESTERONE IN A SINGLETON PREGNANCY WITH A SHORT CERVIX: AN ANALYSIS OF EFFICACY BASED ON THE LEARNING CURVE AND CUMULATIVE SUM ANALYSIS (LC-CUSUM) IN A QUASI-RANDOMIZED CLINICAL TRIAL
Bibliographic record
Abstract
Objective This study aims to determine the performance of cervical pessary in singleton pregnancies with a short cervix based on the learning curve. Design, Settings, Population, and Methods Between 2011 and 2018, 128 singleton gestation between 18th to 24th weeks with a short cervix (<25mm) were referred to our quasi-randomized trial. All cases were treated with progesterone, and, when available, cervical pessary was also offered. Three groups were created for statistical analysis: Group 1 (n=33), treated with progesterone-only; Group 2 and Group 3, treated with cervical pessary plus progesterone. Group 2, included the first cases (n=30), defined by the learning curve and cumulative sum analysis (LC-CUSUM), while Group 3, included the subsequent (n=65). Our outcome was delivery before 34 weeks. Main outcome measures and Results LC-CUSUM demonstrated that 30 patients achieved learning. The preterm birth rate before 34 weeks was 27.3% in Group 1, 20% in Group 2, and 4.6% in Group 3. There was no significant difference in the Group 1/Group 2 comparison (OR 1.10, P=0.945); the Group 1/Group 3 comparison, the difference was significant (OR 0.08, P=0.003). Conclusion LC-CUSUM determined 30 pessaries to achieve the best pessary performance. Cervical pessary plus progesterone can reduce the preterm birth before 34 weeks in patients with a short cervix. Funding This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001 Keywords Preterm birth; learning curve; cervical pessary; vaginal progesterone; singleton pregnancy; short cervix; transvaginal ultrasound.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".