Osteoarthritis Across Joint Sites in the Million Veteran Program Cohort: Insights From Electronic Health Records and Military Service History
Bibliographic record
Abstract
OBJECTIVE: To characterize the relationship between the frequency of idiopathic osteoarthritis (OA) and characteristics including demographics, comorbidities, military service history, and physical health in a veteran population. METHODS: We performed a cohort study in the Million Veteran Program (MVP) using International Classification of Diseases, 9th and 10th revision codes to define the frequency of site-specific OA across 3 joints or unspecified OA in veterans with respect to demographics (eg, age, sex, race and ethnicity), military service data, detailed electronic health records, military branch, and war era. RESULTS: We validated previous reports of sex- and age-dependent differences in OA frequency, and we identified that unspecified OA was associated with a higher frequency of 16 Deyo-Charlson comorbidities. These associations generally persisted within each isolated joint site-specific OA. Depending on military branch, prior military engagement was differentially associated with the frequency of OA. Prior United States Army and Navy service were associated with higher and lower risk, respectively, of OA across all joint sites; however, multivariable-adjusted models adjusting for a range of covariates, including age, sex, and ancestry, reversed the apparent protective effect of prior Navy service. CONCLUSION: These findings highlight the breadth of factors associated with OA in the MVP veteran population and suggest that physical status may be a modifiable risk factor for OA. This work may help in the design of strategies to optimize appropriate detection, intervention, treatment, and even rehabilitation for OA in veterans and the general population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".