Quality of Non‐Surgical and Non‐Pharmacological Knee Osteoarthritis Care in the Maritimes
Bibliographic record
Abstract
OBJECTIVES: To evaluate the quality and types of care individuals with mild-to-moderate knee osteoarthritis receive in the Canadian Maritime provinces, and determine associations with demographic, social, and patient-reported factors. METHODS: Individuals with knee osteoarthritis were invited to complete a healthcare quality survey based on the British Columbia Osteoarthritis (BC OA) survey. The cross-sectional descriptive observational survey assessed four healthcare quality indicators: advice to exercise, advice to lose weight, assessment of ambulatory function, and assessment of non-ambulatory function. Pass-rates were calculated overall and for each quality indicator. Binary logistic regressions determined associations between quality indicators and demographic, social, and patient-reported outcomes. Patient-reported use of exercise and diet as arthritis treatments were added to the quality indicator eligibility criteria as a sensitivity analysis. RESULTS: and were 77% female. The overall pass rate was 42.9% using the BC OA criteria, and 49.3% in the sensitivity analysis. Individual quality indicator pass-rates ranged from 4.3% for non-ambulatory function to 85.7% for ambulatory function assessments. The sensitivity analysis increased pass-rates for advice to exercise (61.9%-69.3%) and advice to lose weight (27.9%-35.1%). Pass-rates were not driven by demographic, social, or patient-reported factors. CONCLUSIONS: Over half of individuals with mild-to-moderate knee osteoarthritis did not receive recommended core treatments in the Maritimes, highlighting a need to improve care for this patient group. Quality indicators should be routinely evaluated to determine whether clinical care aligns with best practice guidelines.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".