Developing a streamlined risk-adjusted cesarean section rate model for evaluation of obstetrical quality across hospitals by using EHRs: A provincial-scale multicenter retrospective study
Bibliographic record
Abstract
Objective: This study aims to explore a streamlined risk-adjusted cesarean section rate (RCSR) model and to compare its practical application effects with the traditional RCSR models. Methods: Utilizing obstetric electronic health record (EHR) data from provincial multicenter hospitals, this study establishes a streamlined RCSR model alongside the traditional RCSR model and evaluates the efficacy of both models. Subsequently, the RCSRs of 56 hospitals within the province are calculated and ranked using both models. The consistency of these rankings is then quantified using Kendall's tau coefficient of concordance. Result: Comparison of model effectiveness evaluation of the traditional RCSR model versus the streamlined RCSR model is as follows: AUC (0.840 vs 0.839), accuracy (0.875 vs 0.872), sensitivity (0.690 vs 0.685), specificity (0.898 vs 0.892), positive predictive value (0.908 vs 0.903), negative predictive value (0.664 vs 0.660), and Brier score (0.069 vs 0.067). In the test of the consistency of hospital rankings based on two models, Kendall's tau coefficients were observed to be 0.979 (year 2017), 0.978 (year 2018), and 0.978 (year 2019) over a span of 3 years, with an aggregate coefficient of 0.974. Conclusion: In the realm of model performance evaluation as well as the pragmatic application within hospital settings, the streamlined model exhibits a substantial congruence with the traditional model. Therefore, the streamlined model can effectively serve as a viable surrogate for the traditional model, potentially establishing itself as a refined paradigm for the appraisal of quality in obstetric healthcare services.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".