Assessment of factors affecting the duration and quality of tobacco dependence remissions based on the annual reports of tobacco dependence treatment rooms of the Russian Federation
Bibliographic record
Abstract
Objectives One of the relevant and significant directions of improving the quality of tobacco dependence therapy is the ability to predict the duration of abstinence from Smoking on the basis of clinical evaluation of therapeutic remissions. Aim Comparative study of the results of complex therapy of tobacco dependence in 18 regions of the Russian Federation with the analysis of clinical features of emerging therapeutic remissions. Results The results of tobacco dependence therapy, conducted by psychiatrists-narcologists in 18 regions of the Russian Federation evaluated in 6 and 12 months after the anti-nicotine therapy, were investigated. The effectiveness was characterized by quitting, ranged from 38.8% to 68% after therapy course, from 29.5% to 50% - after 6 months, from 25.3% to 38% - after 12 months. The best results were in 8 regions where the combined therapy of tobacco dependence was carried out: the combination of nicotine replacement and receptor therapy with small doses of psychotropic drugs, psychotherapy, non-drug therapies. Factors that worsen the prognosis - pharmacoresistance to drugs for nicotine dependence therapy (Nicorette, Champix), the presence of incomplete remission after quitting. Conclusions New directions to increase the efficiency of results of treatment of tobacco dependence – the improvement of clinical and diagnostic evaluation of Smoking patients, the use of complex pathogenetic therapy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".