International Expert Consensus on Relevant Health and Functioning Concepts to Assess in Users of Tobacco and Nicotine Products: Delphi Study
Bibliographic record
Abstract
BACKGROUND: A Delphi study was conducted to reach a consensus among international clinical and health care experts on the most important health and functioning self-reported concepts when evaluating a switch from smoking cigarettes to using smoke-free tobacco and/or nicotine products (sf-TNPs). OBJECTIVE: The aim of this research was to identify concepts considered important to measure when assessing the health and functioning status of users of tobacco and/or nicotine products. METHODS: Experts (n=105), including health care professionals, researchers, and policy makers, from 26 countries with professional experience and knowledge of sf-TNPs completed a 3-round, adapted Delphi panel. Online surveys combining quantitative (MaxDiff best-worst scaling and latent class analysis) and qualitative assessments were used to rank and achieve alignment on the importance of 69 health and functioning concepts. All experts participating in round I completed round II, and 101 (95%) completed round III. RESULTS: The round I analysis identified 36 (52%) out of 69 concepts that were refined for the round II assessment. The highest-ranked concepts reflected health-related impacts, while the lowest-ranked ranked concepts were related to aesthetics and social impacts. Round II ranking reinforced the importance of concepts relating to health impacts, and the analysis resulted in 20 concepts retained for round III assessment. In round III, the 4 highest-ranked concepts were cardiovascular symptoms, shortness of breath, chest pain, and worry about smoking-related diseases and impact on general health, and they made up 50% of the total score in the MaxDiff analysis. Experts reported likelihood of seeing measurable levels of change in the final 20 concepts with a switch to an sf-TNP. The majority of experts felt it was "likely" or "extremely likely" to observe changes in concepts such as gum problems (74/101, 73%), phlegm or mucus while coughing or not coughing (72/101, 71%), general perception of well-being (72/101, 71%), and throat irritation or sore throat (72/101, 71%). Latent class analysis revealed subgroups of experts with different perceptions of the relative importance of the concepts, which varied depending on professional specialty and geographic region. For example, 74% (14/19) of oncologists aligned with the subgroup prioritizing physical health symptoms, while 71% (12/17) of experts from Asia aligned with the subgroup considering both physical health and psychosocial aspects. CONCLUSIONS: This study identified key concepts to be considered in the development of a new measurement instrument to assess the self-reported health and functioning status of individuals using sf-TNPs. The findings contribute to the scientific evidence base for understanding and evaluating both the individual and public health impacts of sf-TNPs.
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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 | 0.211 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".