Cervical length as a predictor of pre-term birth in twin gestations
Bibliographic record
Abstract
Abstract The aim of this study was to determine the predictive value of cervical length as a risk factor for spontaneous pre-term birth in twin gestations. A retrospective chart review was carried out on patients with twin pregnancies referred to our multiples' clinic. Cervical length was measured by transvaginal ultrasonography. Patients with an indicated pre-term delivery or intervention were excluded from the analysis. Outcomes included preterm delivery < 28 and < 35 weeks gestation. After extracting the data, 2 × 4 tables were constructed. Likelihood ratios were then generated for cervical lengths ≤2.0 cm, ≤2.5 cm, ≤3.0 cm, and > 3.0 cm. Because of the limited number of measurements taken < 25 weeks gestation, we elected to collapse the tables, thereby achieving more meaningful results. For measurements taken before 30 weeks gestation, a shorter cervix did predict delivery < 28 weeks gestation (likelihood ratios for cervical lengths ≤2.0 cm, ≤2.5 cm, ≤3.0 cm, and > 3.0 cm were 4.43, 1.94, 0.97, and 1.02, respectively). The probability of preterm delivery < 35 weeks gestation increased with decreasing cervical length (likelihood ratios for cervical length ≤2.0 cm, ≤2.5 cm, ≤3.0 cm, and > 3.0 cm were 2.58, 1.66, 1.38, and 0.81, respectively). A shorter cervix measured before 30 weeks gestation was a stronger predictor of preterm delivery < 28 weeks compared to < 35 weeks gestation. Cervical length was not predictive of preterm delivery if measured after 30 weeks. Cervical length is predictive of preterm delivery < 28 weeks and < 35 weeks gestation when measured before 30 weeks gestation. No trend was seen when measured after 30 weeks gestation. A prospective study is currently underway to confirm these results. Twin Research (2000) 3, 213–216.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".