GRADUAL NICOTINE TAPERING STRATEGIES FOR SMOKING CESSATION: CHALLENGES AND OPPORTUNITIES
Bibliographic record
Abstract
Smoking remains the leading cause of preventable death worldwide. Nicotine Replacement Therapies (NRTs) are the most commonly used smoking cessation medications. However, the scope of these treatments is limited: drop-out rates are high and their effectiveness is modest. We believe that nicotine tapering plays an important role in overall smoking cessation efficacy. However, each of the NRTs currently on the market have been approved on the basis of either a unique tapering strategy or none at all. Therefore, it is unknown whether improved efficacy and safety outcomes could have been achieved by using different approaches. Moreover, dosing regimens of marketed NRTs lack personalization. They are based on a “one-size-fits-all” approach, which is not optimal given that smokers represent a highly heterogeneous group. The emergence of digital health and Electronic Nicotine Delivery Systems (ENDS), which have demonstrated superior outcomes compared to NRTs in terms of smoking cessation rates, give way to the development of new innovative ways to gradually reduce nicotine in a personalized fashion, without the limitations of currently approved NRTs.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".