Discordance Between Self-report of Physician Diagnosis and Administrative Database Diagnosis of Arthritis and Its Predictors
Bibliographic record
Abstract
OBJECTIVE: To study predictors of discordance between self-reported physician diagnosis and administrative database diagnosis of arthritis. METHODS: A cohort of all veterans who utilized Veterans Integrated Service Network (VISN)-13 medical facilities were mailed a questionnaire that included patient self-report of physician diagnosis of arthritis and questions regarding demographics, functional limitation, and SF-36V (a validated version of the Medical Outcomes Study Short-Form 36). Kappa coefficient was used to assess the extent of agreement between self-report of physician diagnosis and administrative database definitions that incorporated International Classification of Diseases (ICD) codes and use of medications for arthritis. We identified predictors of overall discordance between self-report and administrative database diagnosis using multivariable logistic regression analyses. RESULTS: Among 70,334 eligible veterans surveyed, 19,749 subjects had an ICD diagnosis of arthritis in the administrative database in the year prior to the survey; 34,440 answered the arthritis question and 18,464 self-reported a physician diagnosis of arthritis. Kappa coefficient showed slight to fair agreement of 0.19-0.32 between self-report and administrative database definitions of arthritis. We found significantly higher overall discordance among veterans with more comorbidities, greater age, worse functional status, lower use of outpatient and inpatient services, lower education level, and among single medical-site users. CONCLUSION: Low level of agreement between self-report and database diagnosis of arthritis and its significant association with patient demographic, clinical, and functional characteristics highlights the limitation of use of these strategies for identification of patients with arthritis in epidemiological studies.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".