Thiopental Does Not Produce Hyperalgesia: A Laboratory Study Using Two Human Experimental Pain Models
Bibliographic record
Abstract
OBJECTIVE: Past investigations assessing the effects of thiopental on pain are conflicting. Although several studies demonstrate hyperalgesia as a result of barbiturate administration, others show analgesia. Our objective was to assess the effects of an infusion of the GABAA agonist thiopental, compared with placebo, in healthy participants on two subjective experimental pain paradigms: noxious electrical stimulation and intradermal capsaicin. METHODS: For electrical stimulation, the milliamps required to achieve pain threshold and tolerance were recorded, and the percent change from baseline was determined for each infusion condition. In the intradermal capsaicin condition, the area of hyperalgesia was determined by von Frey technique pre- and postinfusion, and the percent change in the area of hyperalgesia was calculated. RESULTS: Though thiopental infusion resulted in an increase in the electrical stimulation current required to elicit pain threshold or reach pain tolerance when compared with baseline, this finding was not statistically significant. In the intradermal capsaicin condition, there was a statistically significant difference in overall pre- and postinfusion pain interpretation, as measured by the McGill Pain Questionnaire (P < 0.05), but there was no significant difference in area of hyperalgesia. CONCLUSIONS: In this human study of thiopental's effects on two experimental pain models, our results show that thiopental does not induce hyperalgesia.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".