Intrapartum amnioinfusion for meconium‐stained amniotic fluid: a systematic review of randomised controlled trials
Bibliographic record
Abstract
BACKGROUND: Amnioinfusion (AI) is thought to dilute meconium when present in the amniotic fluid and so reduces the risk of meconium aspiration. OBJECTIVES: To evaluate if AI reduces meconium aspiration syndrome (MAS) and other indicators of morbidity in babies born to women with meconium-stained amniotic fluid (MSAF). SEARCH STRATEGY: PubMed, Medline, EMBASE, and the Cochrane Controlled Trials Register from January 1980 to May 30, 2005, using the keywords 'amnioinfusion' and 'meconium'. SELECTION CRITERIA: Randomised trials comparing AI with no AI for women in labour with MSAF. Trial quality was evaluated using pre-established criteria. DATA COLLECTION AND ANALYSIS: The following morbidity indicators were assessed: MAS, 5-minute Apgar score < 7, arterial cord pH < 7.2, and caesarean section. Studies were stratified according to the level of peripartum surveillance (standard versus limited). Typical relative risks (RRs) with their 95% confidence intervals were calculated for each outcome using a random effects model. MAIN RESULTS: In clinical settings with standard peripartum surveillance, we found no evidence that AI reduced the risk of MAS (RR 0.59, 95% CI 0.28-1.25), 5-minute Apgar score < 7 (RR 0.90, 95% CI 0.58-1.41), or caesarean delivery (RR 0.89, 95% CI 0.73-1.10). In clinical settings with limited peripartum surveillance, AI appeared to reduce the risk of MAS (RR 0.25, 95% CI 0.13-0.47). CONCLUSION: In clinical settings with standard peripartum surveillance, the evidence does not support the use of AI for MSAF. In settings with limited peripartum surveillance, where complications of MSAF are common, AI appears to reduce the risk of MAS. However, this finding requires confirmation by further studies.
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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 | 0.022 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.011 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".