Treat-to-target strategy for knee osteoarthritis. International technical expert panel consensus and good clinical practice statements
Bibliographic record
Abstract
BACKGROUND: In this work, we aimed to establish a clinical target in the management of knee osteoarthritis (KOA) and to propose good clinical practice (GCP) statements for carrying out a treat-to-target strategy. METHODS: A steering committee of seven experts had formulated a provisional set of recommendations that were exposed for discussion and modification to a technical expert panel (TEP) of 25 multidisciplinary experts from Europe, North America, South America and Asia. The level of evidence and strength of each recommendation was discussed. The TEP formulated overarching principles and GCP statements based on the level of agreement for each item with a vote using a 10-point numerical scale. RESULTS: Two overarching principles and 10 GCP statements were formulated by the TEP. These GCP statements suggest: treatment should achieve clinical improvement bringing the patient to the Patient Acceptable Symptom State (PASS); pharmacological and nonpharmacological treatment should begin as early as possible, with an early diagnosis of symptomatic KOA; the patient should be evaluated every 3-6 months; risk factors of KOA progression should be identified and managed with patients at the beginning of the treatment and monitored regularly; treatment should be adapted according to patient phenotype and disease severity; healthy lifestyle must be promoted and monitored. The level of agreement average ranged from 8.7 to 9.6 on scale. CONCLUSIONS: The proposed overarching principles and GCP statements have the aim of involving patients, general practitioners and multidisciplinary specialists in sharing a therapeutic treat-to-target strategy for KOA management based on the best evidence and expert opinions.
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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 | 0.352 | 0.340 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".