Tabacologie: mise au point 2015 [Smoking cessation: update 2015].
Bibliographic record
Abstract
La prévalence mondiale du tabagisme est environ cinq fois plus importante chez les hommes que chez les femmes, toutefois cet écart tend à s'égaliser. En ce qui concerne les conséquences sur la santé du tabagisme, les femmes semblent plus susceptibles que les hommes. Elles sont notamment plus à risque de présenter certains cancers pulmonaires ou de décéder de maladies cardiovasculaires. Si les hommes sont moins enclins à demander de l'aide pour arrêter de fumer, les femmes quant à elles ont moins de succès dans leurs tentatives d'arrêt et les traitements semblent moins efficaces chez ces dernières. Des interventions d'aide à l'arrêt et des mesures de prévention du tabagisme adaptées aux spécificités de genre ont le potentiel d'améliorer la prise en charge des fumeurs et de diminuer les disparités de genre en santé. Smoking remains a major public health problem in Switzerland and is responsible for about 9000 deaths annually. In 2013, a quarter of the Swiss population (15 and over) were smokers and more than half of them wanted to quit smoking. This article provides an update of Swiss clinical practice guidelines published in 2011 and covers several new features, including views regarding smoking reduction, gradual quitting, use of nicotine replacement therapy for a short period prior to quitting, nicotine mouth spray marketing and the reimbursement of varenicline and bupropion treatments (under certain conditions) by basic health insurance. An algorithm summarizes the different stages of management of patients who smoke.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".