Evidence‐based guidelines for intra‐articular injection in knee osteoarthritis: Formulating and evaluating research questions
Bibliographic record
Abstract
OBJECTIVE: To formulate and evaluate clinical questions about intra-articular injection for knee osteoarthritis in developing clinical practice guidelines and to introduce a new methodology for framing relevant questions. METHODS: We framed the clinical questions and evaluated the importance of these questions according to the following four steps: (a) first round questionnaire survey intended to get the clinical questions from doctors; (b) evaluating importance of questions via second round questionnaire survey intended to summarize and rank the clinical questions; (c) consensus conference was conducted by clinical and methodological experts; and (d) confirm the important clinical questions according to PICO (Patients, Intervention, Comparison and Outcomes) principles. RESULTS: After the first round questionnaire survey, the number of clinical questions was 26. Thirteen of these 26 questions were regarded as important questions by the second questionnaire survey. Ultimately, the 13 important clinical questions were determined in a consensus conference. All included questions were deconstructed by clinical experts and methodologists based on the PICO principles. CONCLUSION: The present study describes an approach about the selection of clinical questions and importance evaluation. Relevant important clinical questions about intra-articular injection for knee osteoarthritis are determined according to the methodology. It could help other guideline developers to utilize this method to frame clinical questions.
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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 | 0.450 | 0.633 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".