International Classification of Diseases (ICD)-coded obesity predicts risk of incident osteoporotic fracture
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Bibliographic record
Abstract
International Classification of Diseases (ICD) codes have been used to ascertain individuals who are obese. There has been limited research about the predictive value of ICD-coded obesity for major chronic conditions at the population level. We tested the utility of ICD-coded obesity versus measured obesity for predicting incident major osteoporotic fracture (MOF), after adjusting for covariates (i.e., age and sex). In this historical cohort study (2001-2015), we selected 61,854 individuals aged 50 years and older from the Manitoba Bone Mineral Density Database, Canada. Body mass index (BMI) ≥30 kg/m2 was used to define measured obesity. Hospital and physician ICD codes were used to ascertain ICD-coded obesity and incident MOF. Average cohort age was 66.3 years and 90.3% were female. The sensitivity, specificity and positive predictive value for ICD-coded obesity using measured obesity as the reference were 0.11 (95% confidence interval [CI]: 0.10, 0.11), 0.99 (95% CI: 0.99, 0.99) and 0.79 (95% CI: 0.77, 0.81), respectively. ICD-coded obesity (adjusted hazard ratio [HR] 0.83; 95% CI: 0.70, 0.99) and measured obesity (adjusted HR 0.83; 95% CI: 0.78, 0.88) were associated with decreased MOF risk. Although the area under the receiver operating characteristic curve (AUROC) estimates for incident MOF were not significantly different for ICD-coded obesity versus measured obesity (0.648 for ICD-coded obesity versus 0.650 for measured obesity; P = 0.056 for AUROC difference), the category-free net reclassification index for ICD-coded obesity versus measured obesity was -0.08 (95% CI: -0.11, -0.06) for predicting incident MOF. ICD-coded obesity predicted incident MOF, though it had low sensitivity and reclassified MOF risk slightly less well than measured obesity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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