OP10.07: The predictive value of sonographic cervical length in triplet pregnancies?
Bibliographic record
Abstract
While sonographic cervical length (CL) at mid-trimester has been shown to predict preterm birth (PTB) in singleton and twins, data regarding the predictive value of CL in triplets is limited. Our aim was to assess the predictive accuracy of CL in triplet pregnancies. Retrospective study of women with triplets and twins who were followed in a tertiary referral centre and underwent serial sonographic measurement of cervical length between 16 and 32 weeks. The change in CL along gestation in triplets was compared to that observed in twins. The predictive accuracy of CL was determined at 4 time periods along gestation: 18–20 (period 1), 21–24 (period 2), 25–27 (period 3) and 28–32 (period 4) weeks. 1) Overall 442 measurements of CL were available from 86 women with triplet pregnancies. 2) The rate of CL shortening was faster in triplets compared with twins (figure 1A), as well as among triplets that delivered before 34, 32 or 30 weeks compared with those who did not (figures 1B, 1C and 1D). 3) The correlation between CL and gestational age at delivery was highest during periods 3 and 4 at (r = 0.57–0.58) compared with periods 1 and 2 (r = 0.19 and 0.34, respectively, p < 0.001). 4) CL at 18–20 weeks (period 1) was not predictive of PTB. 5) CL at > =25 weeks (periods 3 and 4) had the highest predictive value for PTB, with a PPV of 78–87% and NPV of 54–68%. Supporting information can be found in the online version of this abstract Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".