Diagnostic performance of self-reported knee crepitus using a Knee injury and Osteoarthritis Outcome Score item
Bibliographic record
Abstract
OBJECTIVE: To assess the diagnostic performance of a single Knee injury and Osteoarthritis Outcome Score (KOOS) item in evaluating the presence of knee crepitus. DESIGN: All 184 participants aged 18-40 years with a symptomatic knee, 9-36 months following anterior cruciate ligament reconstruction (ACLR) who were prospectively enrolled in a post-traumatic knee osteoarthritis trial (ACTRN12620001164987) were included. Participants completed the KOOS and underwent physical examination for knee crepitus at baseline. Self-reported knee crepitus (index test) of the ACLR knee was defined as a response of "often" or "always" on item S2 of the KOOS-Symptom subscale (KOOS-S2: Do you feel grinding, hear clicking or any other type of noise when your knee moves?). The presence of knee crepitus on physical examination (reference standard) was defined as continuous grinding, crunching or crackling during three consecutive squats with the investigator's palm placed lightly over the patella. Sensitivity, specificity, positive (LR+) and negative likelihood ratios (LR-), and positive (PPV) and negative predictive values (NPV), with 95% confidence intervals (CI), were calculated. RESULTS: On physical examination, 113 (62%) participants had knee crepitus, and 71 (39%) met the criteria for self-reported knee crepitus. KOOS-S2 demonstrated a specificity of 73% (95%CI 61%-83%), sensitivity of 47% (95%CI 37%-57%), LR+ of 1.75 (95%CI 1.14-2.70), LR- of 0.72 (95%CI 0.58-0.91), PPV of 74% (95%CI 64%-81%), and NPV of 46% (95%CI 41%-52%). CONCLUSION: KOOS-S2 may be a useful method to rule in the presence of knee crepitus on physical examination in individuals post-ACLR; however, it is inadequate for ruling out this clinical sign.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".