Risk scoring systems for predicting preterm birth with the aim of reducing associated adverse outcomes
Bibliographic record
Abstract
BACKGROUND: Identification of pregnancies that are higher risk than average is important to allow the possibility of interventions aimed at preventing adverse outcomes like preterm birth. Many scoring systems designed to classify the risk of a number of poor pregnancy outcomes (e.g. perinatal mortality, low birthweight, and preterm birth) have been developed, but they have usually been introduced without evaluation of their utility and validity. OBJECTIVES: To determine whether the use of a risk-screening tool designed to predict preterm birth (in combination with appropriate consequent interventions) reduces the incidence of preterm birth and very preterm birth, and associated adverse outcomes. SEARCH METHODS: We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (December 2010), CENTRAL (The Cochrane Library 2010, Issue 4), MEDLINE (1966 to 17 December 2010), EMBASE (1974 to 17 December 2010), and CINAHL (1982 to 17 December 2010). SELECTION CRITERIA: All randomised or quasi-randomised (including cluster-randomised) or controlled clinical trials that compared the incidence of preterm birth between groups that used a risk scoring instrument to predict preterm birth with those who used an alternative instrument, or no instrument; or that compared the use of the same instrument at different gestations. The reports may have been published in peer reviewed or non-peer reviewed publications, or not published, and written in any language. DATA COLLECTION AND ANALYSIS: All review authors planned to independently assess for inclusion all the potential studies we identified as a result of the search strategy. However, we identified no eligible studies. MAIN RESULTS: Extensive searching revealed no trials of the use of risk scoring systems to prevent preterm birth. AUTHORS' CONCLUSIONS: The role of risk scoring systems in the prevention of preterm birth is unknown.There is a need for prospective studies that evaluate the use of a risk-screening tool designed to predict preterm birth (in combination with appropriate consequent interventions) to prevent preterm birth, including qualitative and/or quantitative evaluation of their impact on women's well-being. If these prove promising, they should be followed by an adequately powered, well-designed randomised controlled trial.
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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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".