Quality of care for individuals with osteoarthritis: a longitudinal study
Bibliographic record
Abstract
OBJECTIVES: The objectives of this paper were to investigate quality of care for individuals with osteoarthritis (OA) and to determine if those most in need had the outcome of a total joint replacement (TJR). Key quality indicators were involvement in treatment decisions, appropriate information provision and outcomes of care. METHODS: A longitudinal study was conducted on individuals newly referred to an orthopaedic specialist at one hospital in North West England. A total of 268 participants were recruited consecutively and followed up at 3, 6 and 12 months. Validated measurement tools such as, a Visual Analogue Scale for pain and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) assessed pain and physical functioning. Mean scores on the outcome measures were estimated and plotted over time by joint affected and whether or not the participant had a TJR by 12 months. RESULTS: Most participants (82%) felt that they were involved in the decision about their care, although 21% reported that they had not received a diagnosis of OA. Information was not provided on OA, pain management and exercise to 58%, 65% and 57% of participants, respectively. However, 98% of the 109 having a TJR reported receiving information about the procedure. Among the 118 known not to have had a TJR, pain and physical functioning remained relatively stable over time. CONCLUSION: It appears that patients with the most severe symptoms of pain and physical functioning were selected for TJR. However, care for individuals with OA could be improved by providing standard information on OA in general and pain management and exercise. In particular, effective strategies for the implementation of the research evidence and guidelines are required to improve quality of care.
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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 | 0.005 | 0.009 |
| 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 | 0.001 |
| 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".