Bibliographic record
Abstract
High-frequency stimulation of peripheral nerve bundles is frequently used in clinical tests and physiologic experiments to study presynaptic and postsynaptic effects. To understand the postsynaptic effects, it is important to ensure that each pulse in the train is equally effective in stimulating the presynaptic nerve bundle; however, the optimal interpulse interval (IPI) and the stimulus intensity at which each pulse is equally effective in stimulating the same number of axons are not known. The magnitude of the compound action potential produced by each pulse in a train was tested on the sural nerve of 4 healthy human subjects. The stimulus train (2-4 pulses) was applied to the sural nerve at the lateral malleolus, and neural responses were recorded from just below the knee. With 2-pulse trains, families of curves between IPIs (1-6 ms) and normalized amplitudes of the second response were plotted for different stimulus intensities. Visual inspection of the data showed that the curves fell into 2 groups: with stimulus intensities <2.5x perception threshold (Th), the test response appeared partially at longer IPIs, whereas with stimulus intensities > or = 3x Th, partial recovery of the test response was earlier. The interval for complete recovery was statistically the same for low- and high-intensity stimulation. With more than 2 pulses in a stimulus train (IPI = 5 ms), the amplitude of the compound action potential (CAP) was not affected significantly. These results are important in understanding both the presynaptic and postsynaptic responses when presynaptic axon bundles are stimulated at high frequencies.
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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 | 0.001 | 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) | 0.003 | 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".