General Comorbidity Indicators Contribute to Fracture Risk Independent of FRAX: Registry-Based Cohort Study
Bibliographic record
Abstract
CONTEXT: FRAX® estimates 10-year fracture probability from osteoporosis-specific risk factors. Medical comorbidity indicators are associated with fracture risk but whether these are independent from those in FRAX is uncertain. OBJECTIVE: We hypothesized Johns Hopkins Aggregated Diagnosis Groups (ADG®) score or recent hospitalization number may be independently associated with increased risk for fractures. METHODS: This retrospective cohort study included women and men age ≥ 40 in the Manitoba BMD Registry (1996-2016) with at least 3 years prior health care data and used linked administrative databases to construct ADG scores along with number of hospitalizations for each individual. Incident Major Osteoporotic Fracture and Hip Fracture was ascertained during average follow-up of 9 years; Cox regression analysis determined the association between increasing ADG score or number of hospitalizations and fractures. RESULTS: Separately, hospitalizations and ADG score independently increased the hazard ratio for fracture at all levels of comorbidity (hazard range 1.2-1.8, all P < 0.05), irrespective of adjustment for FRAX, BMD, and competing mortality. Taken together, there was still a higher than predicted rate of fracture at all levels of increased comorbidity, independent of FRAX and BMD but attenuated by competing mortality. Using an intervention threshold of major fracture risk >20%, application of the comorbidity hazard ratio multiplier to the patient population FRAX scores would increase the number of treatment candidates from 8.6% to 14.4%. CONCLUSION: Both complex and simple measures of medical comorbidity may be used to modify FRAX-based risk estimates to capture the increased fracture risk associated with multiple comorbid conditions in older patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".