How often are late preterm births the result of non‐evidence based practices: analysis from a retrospective cohort study at two tertiary referral centres in a nationalised healthcare system
Bibliographic record
Abstract
OBJECTIVE: To determine the proportion, characteristics, and predictors of late preterm birth (LPTB) in relation to evidence-based (EB) and non-evidence based (NEB) indications. DESIGN: Retrospective cohort study. SETTING: Two Canadian tertiary referral centres. POPULATION: All live singleton LPTBs over 1 year from 2010 to 2011, excluding major congenital anomalies. METHODS: Indications for LPTB were classified a priori as EB (i.e. based on practice guidelines or on evidence from randomised controlled trials) or NEB. Data were abstracted from maternal antenatal and labour records. Univariate analyses were completed using Fischer's exact, Pearson's chi-square, or analysis of variance (anova) F-tests. Logistic regression included gestation at birth, delivery provider, previous stillbirth, previous caesarean section, corticosteroid administration, and previous preterm birth as predictors for NEB LPTB. MAIN OUTCOME MEASURES: The proportion, characteristics, and predictors of women with NEB versus EB LPTBs. RESULTS: Of 524 LPTBs, 25.2% (n = 132) were NEB. Logistic regression revealed that NEB LPTBs were less likely if patients were delivered by their own doctor or their doctor's practice partner (OR 0.53, 95% CI 0.34-0.83). However, NEB LPTBs were more likely in women who had experienced a previous stillbirth (OR 2.57, 95% CI 1.20-5.49). CONCLUSIONS: Approximately one-quarter of LPTBs are NEB. Further research is needed to see if a review of the indications for LPTB, and subsequent reduction in NEB LPTBs, translates into improved neonatal outcomes and cost savings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".