Stimulating Aδ and C-Fibers in the Lower Limb With a 980 nm Diode Laser
Bibliographic record
Abstract
Background: Laser evoked potentials are increasingly used to investigate the integrity of the nociceptive system. Laser heat stimuli can activate A δ fibers, activation of C-fibers remains difficult. This study attempts to stimulate A δ and C fibers separately with a ‘ grid ’ to generate respectively late and ultra-late LEPs. A ‘ grid ’ is a thin aluminum plate used as a spatial filter to stimulate C-fibers. Furthermore, study subjects pressed a button upon detecting a laser stimulus which was used to measure reaction times (RT) following diode laser stimulation. Methods: Cutaneous heat stimuli were applied at the Th 12 and L 5 dermatome in s eventeen volunteers . Conduction velocities (CV) were calculated by measuring latencies of P2 and reaction times (RT). Results: Stimulation condition Th 12 no-grid showed a P2 late response at 330 ± 47 ms and L 5 no-grid at 413 ± 53 ms. Mean reaction time during Th 12 no-grid was 537 ± 146 ms, L 5 no-grid 784 ± 334 ms, Th 12 grid 710 ± 195 ms and L 5 grid 1,391 ± 336 ms. During stimulation block Th 12 grid and L 5 grid ultra-late LEPS could not reliably be generated. Median conduction velocities (CV) and their corresponding range were calculated. The median CV RT no grid was 5.8 m/s (range 1.2 - 43.3). The median CV LEP no grid was 13.8 m/s (range 4.7 - 45.4). The median CV RT grid was 1.9 m/s (range 0.8 - 3.7). Ultra-late LEPs could not be generated, although subjects mentioned a long lasting burning pain during Th 12 grid and L 5 grid . Conclusions: This study questions the feasibility of the ‘ grid ’ to reliably generate C-fiber responses. Pressing a button upon laser stimulus detection seems preferable for identifying C-fiber stimulation in the lower limb, whereas for A δ nociceptive pathways laser evoked potentials might be of use. doi: http://dx.doi.org/10.4021/jnr177w
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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 | 0.000 |
| 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 | 0.000 |
| 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".