What Do People With Knee or Hip Osteoarthritis Need to Know? An International Consensus List of Essential Statements for Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To establish priority key messages for patients with osteoarthritis (OA). METHODS: A Delphi survey and priority pairwise ranking activity was conducted. Participants included 51 OA experts from 13 countries and 9 patients (consumers) living with hip and/or knee OA. During 3 Delphi rounds, the panel of experts and consumers rated recommendations extracted from clinical guidelines and provided additional statements they considered important. When ≥70% of panel members agreed a statement was "essential," it was retained for the next Delphi round. The final list of essential statements was reviewed by a consumer focus group and statements were modified for clarity if required. Finally, a priority pairwise ranking activity determined the rank order of the list of essential messages. RESULTS: Eighty-five experts and 15 consumers were invited to participate; 51 experts and 9 consumers completed round 1 of the Delphi survey, and 43 experts and 8 consumers completed the final priority ranking activity. From an original list of 114 statements, 21 statements were rated as essential. Most statements (n = 17) related to nondrug treatment approaches for OA. Study limitations included that >50% of the panel comprised of physical therapists lead to high rankings of exercise and physical activity statements and also that only English-language statements were considered. CONCLUSION: OA experts and consumers have identified and prioritized 21 key patient messages about OA. These messages may be used to inform the content of consumer educational materials to ensure patients are educated about the most important aspects of OA and its management.
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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 | 0.103 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".