Bibliographic record
Abstract
increase during a quit attempt especially in those with a psychiatric illness. Several study limitations exist, the most important of which is the exclusion of those with a substance use disorder within the previous 12 months, secondary to their qualifying primary disorder. Additionally, substance use disorder was not included as a primary qualifying disorder. Considering the extremely high prevalence of smoking among those with dependence on alcohol 11 or drugs, 4 not to mention the high prevalence of substance use among psychiatric populations, this exclusion is extremely disappointing and means the fi ndings cannot be generalised to this population. Still, Anthenelli and colleagues show that although the incidence of neuropsychiatric adverse events during smoking cessation is not zero, the risk of such an event occurring is not signifi cantly increased by smoking cessation medications. It will be of interest to see if the US Food and Drug Administration (and their counterparts in other countries) will remove the black box warning for varenicline and bupropion in light of these fi ndings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.004 |
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".