Diagnosing PCL Injuries: History, Physical Examination, Imaging Studies, Arthroscopic Evaluation
Bibliographic record
Abstract
Isolated posterior cruciate ligament (PCL) injuries are uncommon and can be easily missed with physical examination. The purpose of this article is to give an overview of the clinical, diagnostic and arthroscopic evaluation of a PCL injured knee. There are some specific injury mechanisms that can cause a PCL including the dashboard direct anterior blow and hyperflexion mechanisms. During the diagnostic process it is important to distinguish between an isolated or multiligament injury and whether the problem is acute or chronic. Physical examination can be difficult in an acutely injured knee because of pain and swelling, but there are specific functional tests that can indicate a PCL tear. Standard x-ray's and stress views are very useful imaging modalities but magnetic resonance imaging remains the gold standard imaging study for detecting ligament injuries. Every knee scope should be preceded by an examination under anesthesia. Specific arthroscopic findings are indicative of a PCL tear such as the "floppy ACL sign" and the posteromedial drive through sign. History, physical examination and imaging should all be combined to make an accurate diagnosis and initiate appropriate treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".