Sleep arousal response to experimental thermal stimulation during sleep in human subjects free of pain and sleep problems
Bibliographic record
Abstract
Although the interaction between sleep and pain is generating considerable interest (NIH Technology Assessment Panel, 1996), it is still unknown if chronic pain is the cause or effect of poor sleep. To further this understanding, subjects free of pain and sleep problems need to be studied in order to assess their response to pain during sleep, defined as a behavioral and a physiological state in which sensory processing is altered. (For example, while auditory perception remains active, other sensory inputs are facilitated, attenuated, or suppressed (Velluti, 1997)). The present study provides data on polygraphic responses to cool (24°C), warm (37°C), and heat pain (>46°C) stimuli applied to shoulder skin during different sleep stages: the lighter sleep stage 2, the deep stages 3&4, and REM sleep. Based on evidence from eight subjects, we found that nociceptive heat stimulation evokes a moderate level of cortical arousal during sleep. Specifically, in comparison to the response induced by a warm 37°C non-nociceptive control stimulation, the percentage of cortical arousal responses to heat pain stimuli (>46°C) was statistically greater in the lighter sleep stage 2 (48.3%) than in the deeper stages 3&4 (27.9%). A nocifensive behavioral-motor response was associated with only 2.5% of the 351 heat pain stimuli. Two other markers of sleep quality–sleep stage shift and awakening–were not influenced by the thermal stimuli. None of the subjects demonstrated any burns in the morning following the thermal stimulations applied during sleep. We conclude that the processing of nociceptive inputs is attenuated across sleep stages.
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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 | 0.001 |
| 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".