The Proportion of Birth Asphyxia Associated With Maternal Heart Rate Artifact During Electronic Fetal Monitoring in Labour
Bibliographic record
Abstract
OBJECTIVES: This study aimed to estimate the proportion of birth asphyxia cases associated with delay in delivery (DD) due to maternal heart rate artifact (MHRA). METHODS: This was a retrospective review of Canadian Medical Protective Association closed medico-legal cases of birth asphyxia from 2011 to 2020 in term labour, leading to hypoxic ischemic encephalopathy, cerebral palsy, or stillbirth. The final 2 hours of electronic fetal monitoring (EFM) were analyzed in 10-minute epochs by 3 independent experts using a template for evidence of MHRA judged to have resulted in DD. Records were also assessed for EFM classification, documentation of maternal pulse/MHRA, and labour factors. RESULTS: Thirty-four cases of birth asphyxia were identified. Thirteen cases (38%) were found to have DD due to MHRA, of which 9 (69%) were in the second stage of labour. The average estimated DD was 44.2 ± 21.9 minutes. There was a lower proportion of time epochs with abnormal EFM in the 13 cases with DD versus 21 cases without DD (14.7% vs. 47.3%; OR 0.19 [0.11-0.33]; P < 0.002). Conversely, there was a higher proportion of MHRA (62.9% vs. 5.4%; OR 29.8 [15.5-57.3]; P = 0.002). The maternal pulse was documented in 34% versus 30%, respectively. Medical record review revealed no recognition by the caregivers of the occurrence of MHRA. CONCLUSIONS: Unrecognized MHRA resulting in a falsely reassuring fetal heart rate, mainly in the active second stage, led to DD in more than one-third of birth asphyxia cases. These outcomes may be preventable by education and the routine use of technologies to detect MHRA.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".