“Pronation Compensation Sign” as a New Diagnostic Tool for Carpal Tunnel Syndrome: A Prospective Preliminary Study
Bibliographic record
Abstract
Jean Paul Brutus,1 Thiên-Trang Vo,1 Min Cheol Chang2 1Exception MD, Montreal, Canada; 2Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu, Republic of KoreaCorrespondence: Min Cheol Chang, Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, 317-1 Daemyungdong, Namku, Daegu, 705-717, Republic of Korea, Tel +82-53-620-4862, Email wheel633@gmail.comPurpose: Carpal tunnel syndrome (CTS) is commonly encountered in clinical practice. Diagnostic tools that currently exist include painful provocative maneuvers, invasive nerve conduction studies and the use of tests that require physician’s direct participation in an era of sanitary crisis and virtual consultations. Therefore, having an easily accessible, reliable and practical tool for diagnosing CTS would be highly beneficial. Herein, we investigated the diagnostic value of the “pronation compensation sign” that we described for diagnosing CTS.Patients and Methods: We included 18 hands with and 18 hands without CTS (age: CTS hands = 52.5 ± 13.8 years, non-CTS hands = 43.2 ± 12.3 years; sex ratio: CTS hands = 12:8, non-CTS hands = 9:9). The presence of the “pronation compensation sign” was evaluated in each included hand. The presence of the “pronation compensation sign” were compared between CTS and non-CTS hands using the chi-squared test. Statistical significance was set at p < 0.05. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated of the “pronation compensation sign” for CTS.Results: All 18 hands with CTS showed a positive “pronation compensation sign”, while those without CTS were negative. All 18 hands that were positive for the “pronation compensation sign” were hands with CTS, while those that were negative were hands without CTS. The sensitivity and specificity of the “pronation compensation sign” for diagnosing CTS were both 100%. The PPV and NPV of the “pronation compensation sign” for CTS were both 1.000. The rates of the presence of the “pronation compensation sign” were significantly different between hands with and without CTS (p < 0.001).Conclusion: The “pronation compensation sign” seems a useful tool for diagnosing CTS. We believe that the “pronation compensation sign” will help clinicians diagnose CTS with high diagnostic accuracy.Keywords: carpal tunnel syndrome, pronation compensation sign, diagnosis, accuracy, hand
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".