The Sensitivity and Specificity of Split-Hand Index Using Muscle Sonography
Bibliographic record
Abstract
ABSTRACT: Background: The split-hand index (SHI) (first dorsal interosseous (FDI) × abductor pollicis brevis (APB)/abductor digiti minimi muscle (ADM)) has been suggested as a useful measure for amyotrophic lateral sclerosis (ALS) diagnosis, using electrophysiological and sonographic indices. In the present study, we aimed to explore the specificity of SHI derived by muscle ultrasound (MUS) for the diagnosis of ALS and spinal muscular atrophy (SMA). Methods: Healthy controls (n= 65) were prospectively recruited at the Prosserman Family Neuromuscular clinic at Toronto General Hospital, from October to December 2018. In addition, 181 patients with ALS (n= 91), SMA (n= 33), polyneuropathy (n= 35), and myopathy (n= 22) were prospectively recruited at the neuromuscular clinic at Tel Aviv Sourasky Medical Center, from December 2018 to December 2020. All subjects underwent quantitative sonographic evaluation of muscle thickness, including the right APB, FDI, and ADM muscles. Area under curve (AUC), sensitivity, and specificity were determined for differentiating between groups. Results: Although SHI showed good to excellent accuracy for differentiating each patient subgroup from controls (AUC 0.83–0.92), poorer diagnostic accuracy was shown for differentiating between different patient subgroups (AUC 0.54–0.74). Conclusions: Sonographic SHI is useful for differentiating patients from healthy controls, but might be not specific for motor neuron disease.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".