Diagnostic Accuracy of Magnetic Resonance Imaging (MRI) Knee in detecting Anterior Cruciate Ligament (ACL) Tears assuming Arthroscopy as Gold Standard
Bibliographic record
Abstract
Background: Anterior cruciate ligament is a core ligament of the knee joint and is vulnerable to injuries in sports and athletics. Timely diagnosis of ACL injuries can result in better management and fast repair. This study explores the diagnostic accuracy of Magnetic resonance imaging (MRI) of knee joint in ACL complete tear. Patients and methods: To decide the diagnostic efficiency awareness, particularity, positive predictive values (PPV), negative predictive values (NPV) worth and demonstrative precision of X-ray in contrast with arthroscopy for identification of upper leg tendon tears of the knee. A total 78 patients were considered in this study with suspicion of ACL rupture. Patients underwent both MRI and arthroscopy for the detection of ACL tears. The outcomes of the MRI were compared with the accepted gold standard, arthroscopy. Results: The mean age of the patients enrolled in the study turned out as 45.1 ± 12.8 (p<0.001). ACL tears were presented in 52.6% of the patients according to both arthroscopy and MRI. According to MRI, there were 39 true positives (TP), 2 false positives (FP), 2 false negatives (FN), and 35 true negatives (TN). It was found out that PPV was 90.3%, NPV was 97.2%, sensitivity was 95.12%, specificity was 94.59%, while accuracy was observed as 94.87%. Conclusion: MRI can serve the patients in diagnosis such that it is non-invasive, cheap and can avoid unwanted arthroscopies in case of ACL tears diagnosis. These results also support the fact that MRI can be used as an alternative to arthroscopy in the Pakistani population. These findings can serve to better plan medical facilities in Pakistan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".