Discordance between self-reported arthritis and musculoskeletal signs and symptoms in older women
Bibliographic record
Abstract
BACKGROUND: Arthritis is a gendered disease where women have a higher prevalence and more disability than men with arthritis of the same age. Health survey data is a major source of information for monitoring of the burden of arthritis. The validity of self-reported arthritis and the determinants of its accuracy among women have not been thoroughly studied. The objectives of this study were to: 1) examine the agreement between self-report diagnosed arthritis and musculoskeletal signs and symptoms in community-living older women; 2) estimate the sensitivity, specificity, and predictive values of self-reported arthritis; and 3) assess the factors associated with the disagreement. METHODS: A cross-sectional survey of women was undertaken in 2012-13. The health survey asked women about diagnosed arthritis and musculoskeletal signs and symptoms. Agreement between self-reported arthritis and musculoskeletal signs symptoms was measured by Cohen's kappa. Sensitivity, specificity, and predictive values of self-reported arthritis were estimated using musculoskeletal signs and symptoms as the reference standard. Factors associated with disagreement between self-reported arthritis and the reference standard were examined using multiple logistic regression. RESULTS: There were 223 participants self-reported arthritis and 347 did not. A greater number of participants who self-reported arthritis were obese compared to those who did not report arthritis. Those who reported arthritis had worse health, physical functioning, and arthritis symptom measures. Among the 570 participants, 198 had musculoskeletal signs and symptoms suggesting arthritis (the reference standard). Agreement between self-reported arthritis and the reference standard was moderate (kappa = 0.41). Sensitivity, specificity, and positive and negative predictive values of self-reported arthritis in older women were 66.7, 75.5, 59.2, and 81.0% respectively. Regression analysis results indicated that false-positive is associated with better health measured by the Short Form 36 physical summary score, the Health Assessment Questionnaire disability index, or the Western Ontario and McMaster University Osteoarthritis Index total score; whereas false-negative is negatively associated with these variables. CONCLUSION: While some women who reported diagnosed arthritis did not have recent musculoskeletal signs or symptoms, others with the signs and symptoms did not report diagnosed arthritis. Researchers should use caution when employing self-reported arthritis as the case-definition in epidemiological studies.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".