Use of Routinely Collected Data to Classify Planned Mode of Delivery Among Pregnancies With a Previous Cesarean Delivery: A Validation Study
Bibliographic record
Abstract
BACKGROUND: Counseling on the harms and benefits of a planned vaginal versus a planned repeat cesarean delivery often relies on observational studies using routinely collected (or administrative) data. However, the accuracy of planned (rather than actual) mode of delivery classifications in such data remains unknown. This study aimed to evaluate the validity of an administrative data-based algorithm to identify planned vaginal and planned cesarean deliveries among individuals with a previous cesarean. METHODS: An algorithm based on diagnostic and procedural codes was applied to records from the Nova Scotia Atlee Perinatal Database. Included were individuals with a previous cesarean eligible for a trial of labor between 2017 and 2019. We compared the classification of planned mode of delivery using the algorithm with that determined through review of a random sample of 200 medical charts. We estimated sensitivity, specificity, and predictive values with 95% confidence intervals (CIs). RESULTS: Based on the chart review, 80 deliveries (40%) were planned vaginal deliveries. The algorithm had an estimated sensitivity of 99% (95% CI: 93%, 100%), specificity of 96% (95% CI: 91%, 99%), positive predictive value of 94% (95% CI: 87%, 98%), and negative predictive value of 99% (95% CI: 95%, 100%) for identifying planned vaginal deliveries. CONCLUSIONS: An algorithm based on routinely collected data accurately classified planned vaginal and planned cesarean deliveries among individuals with a previous cesarean. These findings suggest that studies using similar algorithms to inform counseling on planned mode of delivery in this population are minimally impacted by misclassification of this data.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.022 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".