PFM.61 Fetal Monitoring in Non-Obstetric Surgery: Systematic Review of the Evidence
Bibliographic record
Abstract
Use of fetal heart rate monitoring (FHRM) on the labour ward is common but obstetricians are less familiar with its use in theatre for non-obstetric surgery. In comparison, obstetric anaesthesia literature largely supports monitoring as an adjunct to maternal observations. The aim of this study was to systematically review the evidence on intra-operative FHRM during non-obstetric surgery. Literature was searched between 1966 and 2013 for all reports of FHRM in non-obstetric surgery; multiple sources were searched. All studies were considered; those meeting criteria underwent data extraction and quality appraisal. Forty-three cases were identified within the literature, the majority either undergoing maternal general (n = 23) or cardiovascular (n = 17) surgery. Cases were identified either from case reports or case series. Several reports discussed changes with fetal heart rate patterns on induction of anaesthesia, including reduced variability. Nearly all cases of FHRM in cardiovascular surgery reported profound fetal bradycardias on initiation of maternal by pass, which often persisted for the duration of surgery. There were three reports of delivery of the fetus as a result of the FHRM; one of these cases was reported as an inappropriate response to reduced variability. Despite the relatively high numbers of women undergoing non-obstetric surgery during pregnancy, only small numbers are reported in the literature, which may be as a result of literature bias. Practitioners considering FHRM during non-obstetric surgery need to be aware of the reported changes in FHRM with onset of general anaesthesia and maternal cardiopulmonary bypass. Individualisation of the decision to use FHRM is appropriate.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".