Rehabilitation and return-to-play following 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 rehabilitation and return to play (RTP) following 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. Eleven questions were generated on rehabilitation and RTP, 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 11 total questions and consensus statements on rehabilitation and RTP developed from 3 rounds of voting, 0 achieved unanimous consensus, 2 achieved strong consensus, 4 achieved consensus, and 5 did not achieve consensus. Conclusions The statements achieving consensus were related to the benefits of early motion and that concomitant procedures may alter the rehabilitation process. RTP following cartilage-related procedures typically follows a rehabilitation guideline largely dependent on the type of cartilage procedure. The statements that did not reach a consensus were related to specific timing to meet goals.
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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 | 0.178 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".