Nonoperative management of knee cartilage injuries—an international Delphi consensus statement
Bibliographic record
Abstract
Introduction Articular cartilage injuries of the knee are a complex and challenging clinical pathology. Objectives The purpose of this study was to establish consensus statements via a Delphi process on the nonoperative management of knee cartilage injuries. Methods A consensus process on knee cartilage injuries utilizing a modified Delphi technique was conducted. Seventy-nine surgeons across 17 countries participated in these consensus statements. Eight questions were generated on the nonoperative management of knee cartilage injuries, with 3 rounds of questionnaires and final voting occurring. Consensus was defined as achieving 80% to 89% agreement, whereas strong consensus was defined as 90% to 99% agreement, and unanimous consensus was defined as 100% agreement with a proposed statement. Results Of the 8 total questions and consensus statements on nonoperative management developed from 3 rounds of voting, 1 achieved unanimous consensus, 2 achieved strong consensus, 2 achieved consensus, and 3 did not achieve consensus . Conclusions The statements that achieved unanimous or strong consensus related to indications, contraindications, and prognostic factors for nonoperative management of knee cartilage injuries. The statements that did not achieve consensus were primarily related to the use of non–weight-bearing, injections, and biophysical stimulation in the treatment of knee cartilage injuries.
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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 | 0.169 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".