The posteromedial corner of the knee: an international expert consensus statement on diagnosis, classification, treatment, and rehabilitation
Bibliographic record
Abstract
© 2020, European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA). Purpose: To establish recommendations for diagnosis, classification, treatment, and rehabilitation of posteromedial corner (PMC) knee injuries using a modified Delphi technique. Methods: A list of statements concerning the diagnosis, classification, treatment and rehabilitation of PMC injuries was created by a working group of four individuals. Using a modified Delphi technique, a group of 35 surgeons with expertise in PMC injuries was surveyed, on three occasions, to establish consensus on the inclusion or exclusion of each statement. Experts were encouraged to propose further suggestions or modifications following each round. Pre-defined criteria were used to refine item lists after each survey. The final document included statements reaching consensus in round three. Results: Thirty-five experts had a 100% response rate for all three rounds. A total of 53 items achieved over 75% consensus. The overall rate of consensus was 82.8%. Statements pertaining to PMC reconstruction and those regarding the treatment of combined cruciate and PMC injuries reached 100% consensus. Consensus was reached for 85.7% of the statements on anatomy of the PMC, 90% for those relating to diagnosis, 70% relating to classification, 64.3% relating to the treatment of isolated PMC injuries, and 83.3% relating to rehabilitation after PMC reconstruction. Conclusion: A modified Delphi technique was applied to generate an expert consensus statement concerning the diagnosis, classification, treatment, and rehabilitation practices for PMC injuries of the knee with high levels of expert agreement. Though the majority of statements pertaining to anatomy, diagnosis, and rehabilitation reached consensus, there remains inconsistency as to the optimal approach to treating isolated PMC injuries. Additionally, there is a need for improved PMC injury classification. Level of evidence: 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 | 0.202 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| 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".