Practice Evoking the Flexor Carpi Radialis H-Reflex: A Guideline for Proficiency
Bibliographic record
Abstract
The purpose of this research was to identify the number of sessions required for a new investigator to become proficient at evoking an H-reflex in the flexor carpi radialis (FCR), in comparison to an experienced investigator. 31 students from Brock University in the greater Niagara region (16 women M age = 32.2, SD = 8.9 yr.; 15 men M age = 27.8, SD = 7.8 yr.) with no known neurological disorders volunteered and completed two test sessions performed by either an experienced or a novice investigator. In randomized order, both investigators stimulated each subject's median nerve 10 times, once every 15 sec. Each session included the measurement of the subject's flexor carpi radialis maximal M-wave amplitude and H-reflex amplitude and latency with surface electromyographic electrodes. The intraclass correlation coefficients (ICC) indicated an adequate correlation between investigators for both M-wave maximal amplitude and H-reflex at 5% of the M-wave maximal amplitude (.84 and .70, respectively). However, there was a low correlation (.38) between the latency values obtained by the two investigators. The peak-to-peak amplitudes of the H-reflex and M-wave do not appear to be influenced by experience of the tester. The latency of the response, however, appears to have an associated learning curve, improving in consistency with increasing practice of tester.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".