Cesarean Sections for Abnormal Fetal Heart Tracings: Setting Appropriateness Indicators Based on Neonatal Outcome [11N]
Bibliographic record
Abstract
INTRODUCTION: Fetal distress is often suspected solely on atypical/abnormal cardiotocography which is subject to inter-individual variability. We identified C-sections performed for atypical/abnormal cardiotocography and compared neonatal outcomes to C-sections performed for labor dystocia. Indicators of C-section appropriateness were developed and applied. METHODS: A retrospective cohort study was conducted using data from Ontario's Perinatal database. Primary outcomes were C-section rates for fetal distress and neonatal outcomes (arterial pH, Apgars, neonatal resuscitation, NICU admission). A C-section for abnormal/atypical cardiotocography was “appropriate” if one or more of these outcomes occurred: 1-minute Apgar < 3, 5-minute Apgar < 7, arterial pH < 7.20, resuscitation required, or NICU admission. RESULTS: Between 2006-2014, 146,676 primary C-sections were performed; 20% were performed for atypical/abnormal cardiotocography. Compared with C-sections for labor dystocia, there were significant differences in maternal age, parity, induction of labor, and level of care (p < 0.001). Odds of newborn resuscitation (ORadj 1.48, CI 1.41-1.54) and NICU admission (ORadj 2.21, CI 2.08-2.34) were higher when indication was atypical/abnormal cardiotocography. Less than half (42.3%) of these neonates had at least one of the criteria used to determine C-section “appropriateness.” Rate of “appropriate” C-sections differed by size of center (p < 0.001), induction of labor (p=0.0027), oxytocin use (p < 0.001), level of care (p < 0.0001), and fetal surveillance method (p=0.004). CONCLUSION: In the absence of objective measures of intrauterine fetal well-being, C-sections may be performed for fetal distress when they are not required. Developing indicators for C-section appropriateness may guide strategies to reduce C-section rates.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".