Cervical length screening: will we truly reap economic benefits with a universal screening approach?
Bibliographic record
Abstract
Preterm birth is a leading cause of perinatal morbidity and mortality. Despite extensive research using a wide-range of approaches and public efforts, the rate of preterm birth has not changed substantially over the past two decades, other than a rise in late preterm births from the late 1990s that was most likely a result of iatrogenic causes (National Vital Statistics Reports 2010;58:1–32). Thus, the prevention of preterm birth remains one of the single most impactful ways to improve perinatal outcomes, but a successful approach has remained elusive. One approach to the prevention of morbidity is to identify high-risk groups and focus prevention efforts aimed at them. Women with a prior preterm birth are at an increased risk of recurrence, and it has been shown that IM 17-OH Progesterone may reduce the risk of preterm birth in this population (Meis et al. N Engl J Med 2003;348:2379–85). Another high-risk group that has been identified are women with a short cervical length (Iams et al. N Engl J Med 1996;334:567–72). Although it appears that IM 17-OH progesterone does not prevent preterm birth in these women (Grobman et al. Am J Obstet Gynecol 2012;207:390.e1–8), vaginal progesterone may do so (Hassan et al. Ultrasound Obstet Gynecol 2011;38:18–31). The current commentary delves into the literature regarding cervical length screening, potential prophylactic treatments, and the potential economic impact of such treatment. The author concludes that although there does appear to be potential benefit from routine screening, a universal screening approach would be onerous and potentially stress inducing to many of our providers and patients. This inconsistency is reflected in the varying recommendations from the Society for Maternal–Fetal Medicine (SMFM) and American Congress of Obstetricians and Gynecologists (ACOG) in the USA, the Society of Obstetricians and Gynaecologists of Canada (SOGC), the National Institute for Health and Care Excellence (NICE) in the UK, and the Collège National des Gynécologues et Obstétriciens Français (CNGOF). Whereas I can appreciate the concern by the author that it will be a lot of screening for a relatively small reduction, I should point out that cost-effectiveness analyses have supported the use of routine cervical length screening (Einerson et al. Am J Obstet Gynecol 2016;215:100.e1–7). In such analyses, the return on investment, as measured by improved outcomes, has greater value than the resources used. Furthermore there are a number of health conditions that we screen for that do not occur very frequently, and nor do we diagnose them accurately. In obstetrics, these include screening for gestational diabetes, fetal aneuploidy, and syphilis. Important aspects of such screening programmes include the ability to make an accurate diagnosis and the impact of treatment once a patient at high risk is identified. Thus, although cervical length screening requires a lot of training and resources, if the risk of preterm birth is actually decreased in this group of women, it appears to be worth the investment. None declared. Completed disclosure of interests form available to view online as supporting information. 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 | 0.017 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.028 | 0.026 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".