Physical therapy services for older adults with at least moderately severe hip or knee arthritis in 2 Ontario counties.
Bibliographic record
Abstract
OBJECTIVE: Physical therapy (PT) is a recommended treatment for the management of arthritis. We investigated factors related to referral to PT services in people with hip or knee arthritis and describe characteristics of treatment received. METHODS: As part of a longitudinal study of the population aged > or = 55 years with at least moderately severe hip or knee arthritis in 2 Ontario counties (n = 1350), participants were surveyed in the third year of followup about use of PT. Participants were categorized as to whether they had total joint replacement surgery in the past year (TJR group, n = 52) or did not (non-TJR group, n = 1298). Multivariate logistic regression was used to identify determinants of referral to PT considering sociodemographic characteristics, comorbidity, use of prescribed arthritis medication, and arthritis severity (WOMAC summary score). RESULTS: Overall, 18.7% of the cohort was referred to PT in the past year: 65.4% of the TJR group and 16.8% of the non-TJR group. The only significant predictor of PT in the TJR group was current use of prescribed arthritis medication. Greater arthritis severity, current use of prescribed arthritis medication, and greater comorbidity were significant independent predictors of referral to PT for the non-TJR group in multivariate logistic regression. The Ontario Health Insurance Plan paid for the majority of PT received. CONCLUSION: Low rates of referral to PT in the previous year suggest possible underutilization. Further research is needed to examine patterns of use of PT throughout the course of the arthritis disease process and to examine barriers to PT access.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".