An updated algorithm recommendation for the management of knee osteoarthritis from the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: The European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) sought to revisit the 2014 algorithm recommendations for knee osteoarthritis (OA), in light of recent efficacy and safety evidence, in order to develop an updated stepwise algorithm that provides practical guidance for the prescribing physician that is applicable in Europe and internationally. METHODS: Using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) process, a summary of evidence document for each intervention in OA was provided to all members of an ESCEO working group, who were required to evaluate and vote on the strength of recommendation for each intervention. Based on the evidence collected, and on the strength of recommendations afforded by consensus of the working group, the final algorithm was constructed. RESULTS: An algorithm for management of knee OA comprising a stepwise approach and incorporating consensus on 15 treatment recommendations was prepared by the ESCEO working group. Both "strong" and "weak" recommendations were afforded to different interventions. The algorithm highlights the continued importance of non-pharmacological interventions throughout the management of OA. Benefits and limitations of different pharmacological treatments are explored in this article, with particular emphasis on safety issues highlighted by recent literature analyses. CONCLUSIONS: The updated ESCEO stepwise algorithm, developed by consensus from clinical experts in OA and informed by available evidence for the benefits and harms of various treatments, provides practical, current guidance that will enable clinicians to deliver patient-centric care in OA practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it