Differences in cervical length during the second trimester among normal weight, overweight and obese women: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: Maternal obesity has been previously linked to increased risk of preterm birth; however, the actual pathophysiology behind this observation remains unknown. Cervical length seems to differentiate among overweight, obese and extremely obese patients, compared to normal weight women. However, to date the actual association between body mass index and cervical length remains unknown. In this systematic review, accumulated evidence is presented to help establish clinical implementations and research perspectives. Methods: We searched Medline, Scopus, the Cochrane Central Register of Controlled Trials CENTRAL, Google Scholar, and Clinicaltrials.gov databases from inception till February 2023. Observational studies that reported on women undergone ultrasound assessment of their cervical length during pregnancy were included, when there was data regarding their body mass index. Statistical meta-analysis was performed with RStudio. The quality of the included studies was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Results: Overall, 20 studies were included in this systematic review and 12 in the meta-analysis. Compared to women with normal weight, underweight women were not associated with increased risk of CL < 15 mm or < 30 mm and their mean CL was comparable (MD -1.51; 95% CI -3.07, 0.05). Overweight women were found to have greater cervical length compared to women with normal weight (MD 1.87; 95% CI 0.52, 3.23) and had a lower risk of CL < 30 mm (OR 0.65; 95% CI 0.47, 0.90). Conclusion: Further research into whether BMI is associated with cervical length in pregnant women is deemed necessary, with large, well-designed, prospective cohort studies with matched control group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".