Smoking Cessation After Brain Damage Does Not Lead to Increased Depression
Bibliographic record
Abstract
BACKGROUND: There are concerns that varenicline (Chantix/Champix), a prescription medication used to treat smoking addiction, might cause serious neuropsychiatric side effects, such as depression, self-injurious behavior, and suicide. However, the cause of depression and related symptoms in persons who quit smoking after taking varenicline remains uncertain, because smoking cessation itself can cause such symptoms. METHOD: We studied 70 patients with brain lesions: 32 had stopped smoking after suffering their lesion (Quitters) and 38 had kept smoking (Non-Quitters). RESULTS: There was no indication of increased depression in the Quitters compared with the Non-Quitters. The 2 groups, which were statistically indistinguishable on demographic and neuropsychological variables, showed the same rates and levels of severity of depression and related symptoms. Moreover, in a subgroup of 16 Quitters who had stopped smoking immediately after their neurological injury in the context of losing their craving to smoke, rates of depression-related symptoms were no higher than in the other Quitters and the Non-Quitters. CONCLUSIONS: Smoking cessation did not lead to elevated levels of depression in patients with brain lesions, suggesting that psychiatric complications (particularly depression) observed after varenicline use are caused by the medication rather than the smoking cessation itself.
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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 | 0.000 |
| 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".