ACR Appropriateness Criteria® Assessment of Gravid Cervix
Bibliographic record
Abstract
It is well recognized that preterm birth is the leading cause of perinatal mortality and morbidity. There is a significant association between cervix length and preterm birth risk. Most authorities consider a cervical length <3 cm as the lower limit of normal. A cervical length >3 cm has a high negative predictive value for delivery less than 34 weeks. A cervical length of <15 mm is moderately predictive (∼ 70%) of preterm birth within 48 hours. Cervical length is normally distributed and should remain relatively constant until the third trimester. Transabdominal US is the least reliable method of cervical length assessment. The most reliable method of documenting cervical length is transvaginal ultrasound (TVUS). Transperineal US is an alternative for imaging if TVUS is contraindicated, such as with premature rupture of membranes. However, the resolution is decreased compared to TVUS. Short cervix length is the single most important predictive finding for premature delivery. This observation should prompt consultation for high risk obstetrical care and consideration of other management options such as cerclage or activity restriction.The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed biennially by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging procedures by the panel. In those instances where evidence is lacking or not definitive, expert opinion may be used to recommend imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".