What is the J-sign and why is it important?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recurrent lateral patellofemoral instability is a complex condition that requires a thorough evaluation to optimize treatment. The J-sign test is classically part of the physical examination, but its significance and importance remain unclear. This review aims to describe how to perform the test and classify the observation as well as to analyze the most recent literature on its clinical applications. RECENT FINDINGS: The J-sign test has been described as positive (present) or negative (absent), and classified using the quadrant method and the Donnell classification. Suboptimal inter-rater reliability has been shown for both classifications, making comparison between clinicians and studies challenging. The J-sign is most predominantly associated with patella alta, trochlear dysplasia, lateral force vector, and rotational abnormalities. A growing number of studies have shown a correlation between a positive J-sign and lower clinical outcome scores and higher rate of surgical failure. SUMMARY: The J-sign is an important aspect of the physical examination in patients with recurrent lateral patellofemoral instability. Although there is no consensus on how to perform or classify the test, it can be used as a marker of severity of patellofemoral instability and is one of the tools available to guide the treatment plan.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".