Long‐term child follow‐up after large obstetric randomised controlled trials for the evaluation of perinatal interventions: a systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: Although the hope is that many perinatal interventions are performed with an ultimate aim to improve the long-term health and development of the child, long-term outcome is rarely used as a primary end-point in perinatal randomised controlled trials (RCTs). OBJECTIVE: To evaluate how often and with which tools long-term follow-up is performed after large obstetric RCTs. SEARCH STRATEGY: We searched the Cochrane Library for Cochrane reviews published by the Cochrane Pregnancy and Childbirth Group for reviews on interventions that aimed to improve neonatal outcome. Selection criteria Reviews on perinatal interventions that were not performed to improve the condition of the neonate were excluded. We limited our review to RCTs with more than 350 participating women. For each included study, we checked in Web of Science as to whether the researchers had reported on follow-up in subsequent publications. DATA COLLECTION AND ANALYSIS: Relevant information was extracted from these RCTs by two reviewers using a predefined data collection sheet. All information was analysed using SPSS 17.0 (SPSS Inc., Chicago, IL, USA). MAIN RESULTS: We studied 212 reviews including 1837 RCTs on perinatal interventions, 249 (14%) of which included 350 participants. Only 40 of 249 RCTs (16%) followed the children after discharge from the hospital to evaluate the effect of a specific perinatal intervention. The number of RCTs with long-term follow-up remained stable, with 10 of 67 RCTs (15%) reporting follow-up before 1990, 17 of 115 (15%) between 1990 and 2000, and 13 of 67 (19%) after 2000 (P = 0.68). CONCLUSIONS: Only a small minority of large perinatal RCTs report the long-term follow-up of the child. Future obstetric RCTs should consider performing long-term follow-up at the start of the 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 | 0.075 | 0.236 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.018 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".