Accuracy and Reliability of Anterior Cruciate Ligament Clinical Examination in a Multidisciplinary Sports Medicine Setting
Bibliographic record
Abstract
OBJECTIVE: To investigate the accuracy and reliability of anterior cruciate ligament (ACL) clinical examination in a multidisciplinary sports medicine setting. DESIGN: Retrospective review of patient charting. SETTING: Community-based multidisciplinary sports medicine clinic. PATIENTS: One hundred twelve patients with surgically confirmed ACL tear. INTERVENTIONS: Review of therapist, physician, and orthopedic surgeon charting. MAIN OUTCOME MEASURES: Scoring for the anterior drawer, Lachman, and pivot shift tests completed during clinical examination. Coefficient of agreement (P(o)) was calculated for each assessment technique to determine the interrater reliability. Sensitivity of assessment was determined by comparing patient's arthroscopic surgical results against clinician's scoring. RESULTS: On average, P(o) values indicated only moderate levels of interrater reliability (anterior drawer, x = 0.57; Lachman, x = 0.45; pivot shift, x = 0.53), with great variation observed between clinician's scoring for each assessment technique. Accuracy testing demonstrated that the Lachman test had the highest level of sensitivity when administered by orthopedic surgeons (x = 86%) and that sensitivity varied greatly among clinician groups and by assessment technique (range, 15%-87%). CONCLUSIONS: In sports medicine, unreliable or inaccurate clinical examination confounds the clinician's ability to make informed decisions regarding appropriate patient referral and treatment interventions. Our results indicate that levels of accuracy and reliability for clinical examination of the ACL within a multidisciplinary sports medicine setting may be much lower than previously reported within the literature. Further research is needed to clarify whether a standardized approach to ACL clinical examination could enhance levels of accuracy and reliability among clinicians working in a multidisciplinary setting.
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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 | 0.010 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".