Contemporaneous Severity of Symptoms and Functioning Reflected by Variations in Reporting Doctor‐Diagnosed Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis (OA) is acknowledged as an enduring condition; however, in epidemiologic studies, half of the participants who report having OA at one time may report not having it at a subsequent time. The aim of this study was to examine whether variations in reporting doctor-diagnosed OA reflected concurrent fluctuations in indicators of disease severity in middle-aged women. METHODS: Data were from 7,623 participants (ages 50-55 years in 2001) in the Australian Longitudinal Study on Women's Health. Based on self-report of doctor-diagnosed OA at surveys in 2001, 2004, 2007, and 2010, the participants were classified according to pattern of OA reporting (e.g., 0-0-0-0 = "no" on all surveys, 0-1-0-1 = "no-yes-no-yes"). Indicators of disease severity included frequency of joint pain/stiffness, use of antiinflammatory medications, and physical functioning assessed with the Short Form 36. Bar graphs were used to show concurrent variations in OA and markers, and associations were examined using log-linear models. RESULTS: In this sample, 46% reported having OA on at least one survey, with half of these cases reporting not having OA at a later survey. The odds of reporting joint pain/stiffness often (odds ratio [OR] 7.26, 95% confidence interval [95% CI] 7.06-7.47) and taking antiinflammatory drugs (OR 4.44, 95% CI 2.37-8.33) were higher and physical functioning scores were lower (OR 3.75, 95% CI 3.56-3.95) when participants reported having OA. CONCLUSION: Variations in reporting OA coincided with episodic fluctuations in symptoms and functioning. Inconsistent reporting of OA could therefore reflect the presence of symptoms rather than reporting error and should be considered in longitudinal 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 | 0.002 | 0.011 |
| 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 | 0.000 | 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".