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Record W4411992223 · doi:10.1016/j.jtocrr.2025.100874

Tobacco Control and Smoking Cessation–Related Content in Oncology Meetings: A Systematic Scoping Review

2025· article· en· W4411992223 on OpenAlexafffund
Sun Woo Choi, Mukesh Chawla, Payton Catherwood, David C. Chen, Ryan S. Huang, William K. Boateng, Jennifer Do, Maha Khan, Abdulrahman Alghabban, Naa Kwarley Quartey, Srinivas Raman, Meredith Giuliani, William K. Evans, Lawson Eng

Bibliographic record

VenueJTO Clinical and Research Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsMcMaster UniversityCancer Care OntarioPrincess Margaret Cancer Centre
FundersUniversity of TorontoCancer Care Ontario
KeywordsSmoking cessationTobacco controlMedicineOncologyInternal medicineFamily medicineNursingPublic healthPathology

Abstract

fetched live from OpenAlex

Introduction: Despite the importance of smoking cessation in cancer care, it remains unclear how much tobacco control and smoking-related content (TCSCR) is included in major oncology meetings. Developing an understanding of the amount of content can help to improve education and dissemination of the benefits of smoking cessation in cancer care. Methods: We performed a scoping review of TCSCR abstracts and educational sessions using online programs and abstract books from 2018 to 2023 for 12 major oncology meetings across different disciplines and disease sites. Results: < 0.001) differed between abstracts from high-income and low-middle-income countries. Conclusions: Despite the importance of smoking cessation in oncology, TCSCR abstracts and educational content are limited at major oncology meetings. Organizers of oncology conferences should be encouraged to explore strategies to include sessions and attract submissions on these topics, particularly from underrepresented regions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.209
GPT teacher head0.521
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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