Osteoarthritis Measurement in Routine Rheumatology Outpatient Practice (OMIRROP) in Australia: A Survey of Practice Style, Instrument Use, Responder Criteria, and State-Attainment Criteria
Bibliographic record
Abstract
OBJECTIVE: The purpose of the 2007 Osteoarthritis Measurement in Routine Rheumatology Outpatient Practice survey was to describe practice styles, instrument usage, and perceptions of responder criteria and state-attainment criteria in osteoarthritis (OA) management in routine clinical rheumatology practice. METHODS: A 16-item questionnaire (65 subcomponents) was developed, pretested, revised, formatted, and mailed to rheumatologists residing in Australia. Responses were obtained from 136 rheumatologists (response rate 58%). RESULTS: Approximately half the Australian respondents did not follow up their patients with hip and knee OA and two-thirds did not follow up their patients with hand OA. Health status measures (HSM) were infrequently used, even by those respondents who followed their patients with OA longitudinally, and the scores from those HSM that were used, were rarely if ever formally recorded. Respondents rated the following 6 requirements of a measure for use in clinical practice as very important: validity, reliability, responsiveness, simplicity, quick completion, and easy scoring. One-fifth to one-quarter of respondents indicated they did not know quantitatively what constituted a clinically important improvement, or a health state acceptable to patients with OA. The majority of the remainder selected values not closely aligned with published values in the peer review literature. CONCLUSION: While simply describing the health status of the patient is interesting, the more strategic applications are in benchmarking, and using the data to inform shared decision-making and therapeutic goal-setting. The OMIRROP survey suggests that further investigation of interpretation issues are essential, before evaluating the role of quantitative measurement in routine OA clinical practice.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".