Posterolateral corner of the knee: an expert consensus statement on diagnosis, classification, treatment, and rehabilitation
Bibliographic record
Abstract
PURPOSE: To develop a statement on the diagnosis, classification, treatment, and rehabilitation concepts of posterolateral corner (PLC) injuries of the knee using a modified Delphi technique. METHODS: A working group of three individuals generated a list of statements relating to the diagnosis, classification, treatment, and rehabilitation of PLC injuries to form the basis of an initial survey for rating by an international group of experts. The PLC expert group (composed of 27 experts throughout the world) was surveyed on three occasions to establish consensus on the inclusion/exclusion of each item. In addition to rating agreement, experts were invited to propose further items for inclusion or to suggest modifications of existing items at each round. Pre-defined criteria were used to refine item lists after each survey. Statements reaching consensus in round three were included within the final consensus document. RESULTS: Twenty-seven experts (100% response rate) completed three rounds of surveys. After three rounds, 29 items achieved consensus with over 75% agreement and less than 5% disagreement. Consensus was reached in 92% of the statements relating to diagnosis of PLC injuries, 100% relating to classification, 70% relating to treatment and in 88% of items relating to rehabilitation statements, with an overall consensus of 81%. CONCLUSIONS: This study has established a consensus statement relating to the diagnosis, classification, treatment, and rehabilitation of PLC injuries. Further research is needed to develop updated classification systems, and better understand the role of non-invasive and minimally invasive approaches along with standardized rehabilitation protocols. LEVEL OF EVIDENCE: Consensus of expert opinion, Level 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 | 0.221 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".