Stimulus pulse‐width influences H‐reflex recruitment but not H<sub>max</sub>/M<sub>max</sub> ratio
Bibliographic record
Abstract
It has been proposed that pulse-widths of 0.5-1.0 ms should be used to evoke H-reflexes in humans; however, the influence of pulse-width on H-reflex recruitment over a range of stimulus intensities has not been well characterized. We constructed soleus H-reflex vs. M-wave recruitment curves using 50, 200, 500, and 1000 micros pulses in 12 subjects. In contrast to previous findings, changing the pulse-width did not significantly alter maximal H-reflex (H(max)) or M-wave (M(max)) amplitudes or H(max)/M(max) ratios. In fact, the 1000 micros pulses resulted in larger H-reflexes when the M-wave was 5% M(max); smaller M-waves at H(max); and lower H-reflex thresholds compared with 50 micros pulses. These differences reflect a leftward shift in the H-reflex vs. M-wave recruitment curve when using wide vs. narrow pulses and, combined with no change in the H(max)/M(max) ratios, suggest that factors other than antidromic collision in motor axons limit H(max). These results support the idea that 1,000 micros pulses should be used to evoke H-reflexes and suggest that wider pulses may be beneficial to generate contractions with a greater reflex contribution when using neuromuscular stimulation for rehabilitation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".