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Record W4416304962 · doi:10.1101/2025.11.16.25340361

Time-Surrogate Variables Enhance the Association Between Cardiotocographic Features and Intrapartum Hypoxic-Ischemic Encephalopathy

2025· preprint· W4416304962 on OpenAlexaff
Johann Vargas-Calixto, Michael W. Kuzniewicz, Marie‐Coralie Cornet, Tai-Wei Wu, Lawrence Gerstley, John Parker, Philip Warrick, Robert E. Kearney

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Language
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsMcGill University
Fundersnot available
KeywordsFetal heart rateCardiotocographyFetusEncephalopathyCohortAssociation (psychology)Uterine contractionCohort study

Abstract

fetched live from OpenAlex

Abstract Background Interruptions to the flow of oxygenated blood to the fetal brain during labor can lead to hypoxic-ischemic encephalopathy (HIE). Timely preventive interventions for suspected hypoxemia are crucial to avoid progression to neurological injury. Prior research has used features from fetal heart rate (FHR) and uterine activity (UA) signals to predict adverse fetal outcomes. However, these systems focused only on the end of labor, when preventive measures are unlikely effective. Previously, we demonstrated that accounting for proximity to birth improved the association between FHR and UA features and the outcome group. Although proximity to birth cannot be known prospectively, other time-surrogate variables (TSVs) may be as useful. Methods We analyzed intrapartum data from 152,761 vaginal births, comprising 150,813 with healthy outcomes, 1,793 with perinatal acidosis, and 155 with confirmed HIE. Classical and novel FHR features were extracted across labor durations of up to 72 hours. This dataset represents the largest cohort of intrapartum FHR and UA signals to date, both in participant count and signal length. We evaluated four alternative TSVs to evaluate their ability to enhance the association of CTG features and the development of HIE. To do so, we applied information-theoretic methods to quantify their contribution to the association of fetal outcomes with FHR and UA features. Findings Our results show that the cumulative contraction duration and the time from labor onset (TLO) were the most effective TSVs for strengthening the association FHR, UA, and fetal outcome. However, it is more practical to track TLO in clinical settings than a continuous contraction monitoring. Conclusion TLO is the most suitable TSV for prospective intrapartum CTG evaluation. Incorporating it may substantially enhance the performance of automated systems for early detection of intrapartum HIE. Author summary Our study addressed a significant challenge in the development of classifiers to detect fetuses at high risk of developing hypoxic-ischemic encephalopathy (HIE) during labor. Most previous classifiers have concentrated on the end of labor, overlooking the dynamic evolution of FHR and UA patterns. This limits their clinical utility, as preventive interventions late in labor are unlikely to avert adverse fetal outcomes. To address this gap, we evaluated several TSVs that capture the progression of FHR and UA patterns and could support prospective, clinically usable intrapartum classifiers. Analyzing data from over 150,000 births, we found that time since labor onset was the most informative and practical TSV for enhancing the associations between FHR and UA features and the development of HIE during labor. Incorporating this variable would enable classifiers to learn the temporal dynamics of FHR and UA features, potentially improving early identification of fetuses at elevated risk for HIE and supporting more timely, effective interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.249
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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