Improving the acquisition of nociceptive evoked potentials without causing more pain
Bibliographic record
Abstract
Following nociceptive heat or laser stimulation, an early contralateral and later vertex potential can be recorded. Although more indicative of the nociceptive input, the acquisition of the contralateral N1 after contact heat stimulation (contact heat-evoked potentials [CHEPs]) remains difficult. An advantage of contact heat is that the baseline skin temperature preceding peak stimulation can be controlled. Increasing the baseline temperature may represent a novel strategy to improve the acquisition of CHEPs without resulting in more subjective pain to stimulation. A study was undertaken in 23 healthy subjects to examine the effects of increasing the baseline temperature but not the perceived intensity of contact heat stimulation. A combined standard averaging and single-trial analysis was performed to disclose how changes in averaged waveforms related to latency jitter and individual trial amplitudes. By increasing the baseline temperature, the acquisition of N1 was improved among subjects with a low-amplitude response (greater than -4μV) following 35°C baseline temperature stimulation (P<.05). Based on standard averaging, N2/P2 amplitudes were also significantly increased with and without an accompanying change in the rating of perceived pain when the baseline temperature was increased (P<.05). In contrast, automated single-trial averaging revealed no significant difference in N2 amplitude when the baseline temperature was increased to 42°C and the peak temperature reduced. These findings suggest that 2 mechanisms underlie the improved acquisition of CHEPs: increased synchronization of afferent volley, yielding larger-amplitude evoked potentials in response to the same rating of intensity; and reduced inter-trial variability.
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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) | 0.001 | 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".