Risk Factor Profiles for Individuals With Diagnosed <scp>OA</scp> and With Symptoms Indicative of <scp>OA</scp>: Findings From the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVE: The vast majority of published estimates of osteoarthritis (OA) burden are based on an OA diagnosis. These data are limited, as individuals often do not visit a physician until their symptoms are moderate to severe. This study compared individuals with an OA diagnosis to those with OA joint symptoms but without a diagnosis considering a number of sociodemographic and health characteristics. A further distinction was made between individuals with symptoms in one joint site and those with symptoms in multiple joint sites. METHODS: Data are from 23 186 respondents aged 45 to 85 years from the first cycle of the Canadian Longitudinal Study on Aging. A multinomial logistic regression model examined the relationship between sociodemographic- and health-related characteristics and OA status (diagnosed OA, joint symptoms without OA, no OA or joint symptoms). In addition, logistic regression models assessed the relationship between OA status and usually experiencing pain and having some degree of functional limitation. RESULTS: Twenty-one percent of respondents reported a diagnosis of OA, and 25% reported symptoms typical of OA but without an OA diagnosis. Other than being slightly younger, the characteristic profile of individuals with symptoms in two or more joint sites was indistinguishable from that of those with diagnosed OA. CONCLUSION: It may be warranted to consider OA-like multiple joint symptoms when deriving estimates of OA-attributed population health and cost burden.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".