Maternal Heart Rate in Labor and the Potential for Confusion With Fetal Heart Rate [20L]
Bibliographic record
Abstract
INTRODUCTION: A source of error in electronic fetal monitoring occurs where there is confusion between the maternal heart rate (MHR) and the fetal heart rate (FHR). The fetal transducer can detect the MHR rather than the FHR. We studied the MHR during labor to determine the proportion of cases where the MHR was in the fetal range (>110 bpm) and could potentially cause confusion. METHODS: Consecutive cases of low risk patients in labor were studied. Cases required a minimum of 2 hours of archived continuous electronic monitoring of both the MHR (by oxygenation saturation probe) and FHR prior to full dilatation. For individual patients, MHR baseline was averaged over each hour in labor and the results express as the proportion of all MHRs >110 bpm during each hour before and after full dilatation. RESULTS: There were 80 labors included in the study with data available for a total range of 10 hours. In the first stage only 2.5% of labors had an MHR>110 bpm 7 hours prior to full dilation, rising to 15% at full dilatation. In the second stage this rose to 30% after 2 hours and 45% after 3 hours. CONCLUSION: An MHR in the fetal range is common in labor especially in the second stage. The potential for confusion is very significant and this can lead to poor outcomes if an abnormal FHR is misidentified as MHR in the apparently normal range.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".