Validation of an obstetric comorbidity index in an external population
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Bibliographic record
Abstract
OBJECTIVES: An obstetric comorbidity index has been developed recently with superior performance characteristics relative to general comorbidity measures in an obstetric population. This study aimed to externally validate this index and to examine the impact of including hospitalisation/delivery records only when estimating comorbidity prevalence and discriminative performance of the obstetric comorbidity index. DESIGN: Validation study. SETTING: Alberta, Canada. POPULATION: Pregnant women who delivered a live or stillborn infant in hospital (n = 5995). METHODS: Administrative databases were linked to create a population-based cohort. Comorbid conditions were identified from diagnoses for the delivery hospitalisation, all hospitalisations and all healthcare contacts (i.e. hospitalisations, emergency room visits and physician visits) that occurred during pregnancy and 3 months pre-conception. Logistic regression was used to test the discriminative performance of the comorbidity index. MAIN OUTCOME MEASURES: Maternal end-organ damage and extended length of stay for delivery. RESULTS: Although prevalence estimates for comorbid conditions were consistently lower in delivery records and hospitalisation data than in data for all healthcare contacts, the discriminative performance of the comorbidity index was constant for maternal end-organ damage [all healthcare contacts area under the receiver operating characteristic curve (AUC) = 0.70; hospitalisation data AUC = 0.67; delivery data AUC = 0.65] and extended length of stay for delivery (all healthcare contacts AUC = 0.60; hospitalisation data AUC = 0.58; delivery data AUC = 0.58). CONCLUSIONS: The obstetric comorbidity index shows similar performance characteristics in an external population and is a valid measure of comorbidity in an obstetric population. Furthermore, the discriminative performance of the comorbidity index was similar for comorbidities ascertained at the time of delivery, in hospitalisation data or through all healthcare contacts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it