Positive predictive value of maximal posterior joint-line tenderness in diagnosing meniscal pathology: a pilot study.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this prospective study was to determine the positive predictive value (PPV) of the point of maximal posterior joint line tenderness (JLT), as a clinical sign, to diagnose underlying meniscal tears. METHODS: We conducted a prospective study of patients requiring arthroscopic surgery, who consecutively presented to the University of Calgary's Sport Medicine Centre. The femurotibial joint line was palpated for the point of maximal tenderness. We recorded the data on the arthroscopy report. A second examiner (orthopedic sport medicine surgical fellow or sport medicine physician) performed the same protocol. An arthroscopist documented the site of pathology as detected by arthroscopy. RESULTS: We found a PPV of 60.0% and a negative predictive value of 62.5%, suggesting that maximal posterior JLT may be predictive of meniscal pathology. The sensitivity and specificity were 84.6% and 31.2%, respectively (p = 0.155), with Fisher's exact test. The kappa score assessed interobserver reliability and was good at 0.48. Patients with maximal posterior JLT but no meniscal pathology did have other confounding pathology and patients with no maximal posterior JLT who had meniscal pathology usually had confounding knee pathology. CONCLUSIONS: We found a PPV of 60.0% of maximal posterior JLT and meniscal pathology located at the same anatomical site on arthroscopic examination.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".