Treatment after anterior cruciate ligament injury: Panther Symposium ACL Treatment Consensus Group
Bibliographic record
Abstract
Treatment strategies for anterior cruciate ligament (ACL) injuries continue to evolve. Evidence supporting best practice guidelines for the management of ACL injury is to a large extent based on studies with low-level evidence. An international consensus group of experts was convened to collaboratively advance toward consensus opinions regarding the best available evidence on operative vs. non-operative treatment for ACL injury. The purpose of this study is to report the consensus statements on operative vs. non-operative treatment of ACL injuries developed at the ACL Consensus Meeting Panther Symposium 2019. Sixty-six international experts on the management of ACL injuries, representing 18 countries, were convened and participated in a process based on the Delphi method of achieving consensus. Proposed consensus statements were drafted by the Scientific Organizing Committee and Session Chairs for the three working groups. Panel participants reviewed preliminary statements prior to the meeting and provided the initial agreement and comments on the statement via an online survey. During the meeting, discussion and debate occurred for each statement, after which a final vote was then held. Eighty percent agreement was defined a-priori as consensus. A total of 11 of 13 statements on operative v. non-operative treatment of ACL injury reached the consensus during the Symposium. Nine statements achieved unanimous support, two reached strong consensus, one did not achieve consensus, and one was removed due to redundancy in the information provided. In highly active patients engaged in jumping, cutting, and pivoting sports, early anatomic ACL reconstruction is recommended due to the high risk of secondary meniscus and cartilage injuries with delayed surgery, although a period of progressive rehabilitation to resolve impairments and improve neuromuscular function is recommended. For patients who seek to return to straight plane activities, non-operative treatment with structured, progressive rehabilitation is an acceptable treatment option. However, with persistent functional instability, or when episodes of giving way occur, anatomic ACL reconstruction is indicated. The consensus statements derived from international leaders in the field will assist clinicians in deciding between operative and non-operative treatments with patients after an ACL injury.Level of evidence V.
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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 | 0.037 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".