EEG Effects of Smoking: Is There Tachyphylaxis?
Bibliographic record
Abstract
Smoking generally affects the human EEG by reducing low-frequency activity and increasing higher-frequency activity. Using a double-blind design, we sought to determine if acute tolerance (tachyphylaxis) to this effect can be observed. EEG was recorded in overnight-abstaining participants before and after smoking three cigarettes at 40-min intervals in two separate sessions. In one session ('short interval'), all three cigarettes had 'typical' nicotine yields (1.1 mg, FTC method). In the other session ('long interval'), the second cigarette had a very low nicotine yield (0.05 mg; thus making the interval between 1.1-mg cigarettes 80 min). Eyes-closed alpha power decreased and eyes-open beta-2 power increased following smoking each of the 'typical-yield' cigarettes but not the low-yield cigarettes. The decrease in alpha power after smoking the 'typical-yield' cigarette at the 40- and 80-min intervals was less than that following the first cigarette of the day, indicating tachyphylaxis. In contrast, the increase in eyes-open beta-2 power did not differ among cigarettes regardless of the order or interval between 'typical-yield' cigarettes. EEG changes in other frequency bands produced by smoking the first cigarette of the day were not consistent across sessions, making interpretation in terms of tachyphylaxis somewhat problematic. Overall, lower-frequency alpha activity displayed patterns consistent with tachyphylaxis while beta-2 did not.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".