Selective Activation of Small Nerve Fibers for Assessing Carpal Tunnel Syndrome
Bibliographic record
Abstract
Carpal tunnel syndrome (CTS) diagnosis could be improved by selectively activating the different types of nerve fibers traversing the carpal tunnel. The objective of this study is to assess the potential of an anodal blocking technique using tripolar surface electrodes for achieving selective fiber activation and evaluating the severity of CTS. Simulations were performed using McNeal's model to determine the stimulating and blocking thresholds for different diameter/conduction velocity groups of nerve fibers. At 9.36 mA stimulus amplitude all nerve fibers from 9 mum to 20 mum were activated. When the current amplitude was increased further, large nerve fibers started getting blocked while small nerve fibers remained active. By gradually increasing the current amplitude small nerve fibers can be selectively activated without the activation of large fibers. The severity of CTS generally progresses from large to small nerve fibers, hence by comparing normal values of amplitude and peak latency of different nerve fiber diameter groups to the CTS affected median nerve recordings of the patient, severity of this syndrome could be detected.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".