Resolved threatened preterm labour: an opportunity for reducing future prematurity?
Bibliographic record
Abstract
Prematurity remains the most significant obstetrical complication with respect to long-term newborn morbidity. Over the last few years, there has been some progress made in the prediction and reduction of risk of preterm birth (PTB). Prediction has focused on identification of maternal characteristics that increase risk, on the evaluation of cervical length and the use of biomarkers [fibronectin, PAPP A, inhibin, alpha fetoprotein (αFP)]. Of the preventive strategies, the use of progesterones in women perceived to be at risk is now commonplace and cervical cerclage or cervical pessaries are being used in selected cases. Many of the maternal characteristics that increase the risk of PTB are well established. Paramount to date have been the long recognised risk factors of a prior PTB or of prior premature membrane rupture (PROM) (Ekwo et al. Obstet Gynecol 1992;80:166–72). However, somewhat surprisingly, little attention has previously been paid to those women, who in earlier pregnancies did not in fact give birth preterm or suffer PROM, but who did present with episodes of threatened preterm labour (TPL) or premature uterine contractions. That is precisely the focus of this present study (Cho et al. BJOG 2019;126:901–5). From a large population dataset, the authors have demonstrated that, as expected, PTB in a first pregnancy is a risk factor, with an odds ratio (OR) of 8.15, for PTB in the second pregnancy. However, TPL in a pregnancy delivering at term was also associated with PTB, with an OR of 2.21. Both prior PTB and TPL with a term delivery present almost equal risks for women to experience PTL in their subsequent pregnancy, even if that pregnancy proceeds to term [OR 3.83 versus 4.61]. Although having less of an impact than prior PTB, given the larger proportion of women experiencing PTL followed by a term delivery, identification of these women as being an at-risk group identified almost as many of the subsequent PTBs as did prior PTB (6.1 versus 8.7% of all PTBs). Despite this modest benefit in detecting women at risk of PTB, considering all women with a combination of PTB and TPL with term delivery to be at risk of PTB identified a cohort of the study population comprising 4.5% of women, in whom just under 15% of all subsequent PTBs occurred. The follow-on question is how effective our preventive strategies might be in this cohort of women. This offers an ideal opportunity for future study and ideally this should be undertaken before we extend the use of current therapies to include this population of women without supportive evidence to justify that approach. It might be argued that the variable data regarding the mainstays of current preventive strategies, e.g. progesterone therapy (Jarde et al. BJOG 2017;124:1163–73; Norman et al. Lancet 2016;387:2106–16), as well as cerclage, exists because of the extrapolation of indications and the empiric use of treatment strategies in cases where robust data justifying these approaches is lacking. None declared. Completed disclosure of interest form is 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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".