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)
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.
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 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 | 0.039 | 0.145 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".