Cumulative deceleration area: a simplified predictor of metabolic acidemia
Bibliographic record
Abstract
OBJECTIVE: Fetal monitoring, ubiquitous in obstetrics is used to predict and prevent intrapartum fetal injury. Despite decades of education and nomenclature revision, clinicians show low agreement on key elements, including the types of deceleration and hence their presumed etiology. Cumulative deceleration area is not dependent on deceleration type and could potentially mitigate this problem. Although deceleration area has shown promise as a marker of acidemia, no reports have shown how deceleration area evolves in late labor. Advances in computerization allow for direct measurement of deceleration area and standard fetal heart rate (FHR) patterns. The objective of this study was to compare the evolution and discrimination performance of deceleration area and other FHR patterns in late labor in term neonates with metabolic acidemia (MA) and in those with normal cord gases. METHODS: = 1498). Deceleration area and other FHR patterns were summarized and compared in 30-minute segments over the last five hours. Receiver-operating characteristic curves were constructed and AUCs compared. RESULTS: Deceleration area had the highest AUC (0.702, 95% CI 0.655-0.749) and was a superior marker of MA compared to baseline (AUC 0.588, 95% CI 0.530-0.645), baseline variability (AUC 0.611, 95% CI 0.558-0.663), and number of late decelerations (AUC 0.582, 95% CI 0.527-0.637). CONCLUSION: Cumulative deceleration area reduces the necessity to determine deceleration type. In a single number, it objectively quantifies three important aspects of decelerations; frequency, depth and duration and was a superior marker of MA compared to baseline level, baseline variability and number of late decelerations. The acidemia group had higher deceleration area over the last two hours prior to delivery. This result indicates that the cumulative area and persistence of repetitive decelerations is important clinically.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".