Diagnostic Accuracy and Association to Disability of Clinical Test Findings Associated with Patellofemoral Pain Syndrome
Bibliographic record
Abstract
PURPOSE: To investigate the diagnostic accuracy and association to disability of selected functional findings or physical examination tests for patellofemoral pain syndrome (PFPS) in patients with anterior knee pain. METHODS: A sample of 76 consecutive patients with anterior knee pain was further subdivided into PFPS and other diagnoses. Routine physical examination tests were examined in a prospective, consecutive-subjects design for a cohort of patients with anterior knee pain. Diagnostic accuracy findings, including sensitivity, specificity, positive (PPV) and negative (NPV) predictive value, and positive (LR+) and negative (LR-) likelihood ratios, were calculated for each test. PPV and NPV reflect the percentage of time of positive or a negative test (respectively) accurately captures the diagnosis of the condition. LR+ and LR- reflect alterations in post-test probability when the test is positive or negative (respectively). Lastly, associations to disability (International Knee Documentation Committee (IKDC) subjective form) were calculated for each clinical finding. RESULTS: Diagnostic accuracy analyses of individual functional assessment and situational phenomena suggest that the strongest diagnostic test is pain encountered during resisted muscle contraction of the knee (PPV=82%; LR+=2.2; 95% CI: 0.99-5.2). Clusters of test findings were substantially more diagnostic, with any two of three positive findings of muscle contraction, pain during squatting, and pain during palpation yielding the following values: PPV=89%; LR+=4.0 (95% CI: 1.8-10.3). No individual or clustered test findings were significantly associated with the IKDC score. CONCLUSION: Combinations of functional assessment tests and situational phenomena are diagnostic for PFPS and may serve to rule in and rule out the presence of PFPS. Single findings are not related to disability scores (IKDC).
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".