Cervical insufficiency: Re-evaluating the prophylactic cervical cerclage
Bibliographic record
Abstract
Historically, placement of a cervical cerclage was based almost entirely on the obstetrical history. Over the past two decades however, we have recognised that history alone may not be the only indication for cerclage but rather, complementing the obstetrical history with ultrasonographic and biochemical findings may better identify those women who may benefit most from the placement of a cervical cerclage. Review of the literature appears to suggest that the best approach towards the management of a cervical insufficiency is to first categorise women as being either high risk of low risk-based on obstetrical history. Although women with an obstetrical history of at least three 2nd trimester losses are likely to benefit from a prophylactic cerclage than those without this history may better be managed with progesterone and serial cervical length measurements. This approach can in turn be used to identify those women with early cervical shortening that may require an emergency cerclage. Although randomised controlled trials are still lacking, recent studies suggests that this approach may be more effective especially when combined with markers of intra-amniotic inflammation. As for the prophylactic cerclage itself, with the abdominal cerclage being less invasive given the possibility of a laparoscopic placement, it may prove to be a more effective alternative to the conventionally placed McDonald cerclage. This however, remains to be evaluated more carefully.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".