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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".