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Record W2345850213 · doi:10.1158/1940-6215.prev-14-b15

Abstract B15: Second-hand smoke (SHS) and smoking cessation in non-tobacco related cancers

2015· article· en· W2345850213 on OpenAlexaffabout
Lawson Eng, Xin Qiu, Jie Su, M. Catherine Brown, Margaret Irwin, Dan Pringle, Hiten Naik, Chongya Niu, Mary Mahler, Henrique Hon, Kyoko Tiessen, Rebecca Charow, Henry Thai, Valerie Ho, Vivien Pat, Lindsay Herzog, Anthea Ho, Jennifer M. Jones, Doris Howell, David P. Goldstein, Meredith Giuliani, Wei Xu, Peter Selby, Geoffrey Liu

Bibliographic record

VenueCancer Prevention Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsCentre for Addiction and Mental HealthPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineSmoking cessationCancerLogistic regressionLung cancerInternal medicineOdds ratioHazard ratioTobacco smokeEnvironmental healthPathologyConfidence interval

Abstract

fetched live from OpenAlex

Abstract Introduction: Continued smoking after a diagnosis of cancer has been found to lead to poorer treatment response, reduced survival and quality of life and increased risk of second primary cancers. We have previously demonstrated that SHS (exposure at home, with spouses and peers) is a significant barrier to smoking cessation in tobacco-related (lung and head and neck) cancers with adjust odds ratios of 6-9 (PMID: 24419133, 23765604) for quitting 1 year after diagnosis and quitting at any time after diagnosis; relationships stronger than in non-cancer populations. Here, we examined whether this relationship exists in cancers that are not traditionally associated with smoking. Patients and Methods: Cancer survivors from a single tertiary cancer centre, Princess Margaret Cancer Centre (Toronto, Canada) completed a one-time cross-sectional questionnaire assessing their socio-demographics, functional status, smoking history and SHS exposure. Clinico-pathological variables were obtained through review of patient charts. Multivariate logistic regression and Cox-proportional hazard models evaluated the association of SHS with smoking cessation at 1 year after diagnosis and any time after diagnosis, and time-to-quitting respectively, adjusted for significant co-variates. Results: A total of 1011 non-tobacco related cancer survivors were surveyed between 2012 and 2014: 19% breast, 15% gastrointestinal, 16% genitourinary, 12% gynecological, 23% hematologic, 15% other. Median follow-up time after diagnosis was 26 months. Among the 162 patients currently smoking at diagnosis, 35% quit 1 year after diagnosis and 48% quit at any time after diagnosis. None of the 306 ex-smokers and 543 never smokers (re-)started smoking after diagnosis. Home exposure to SHS was found to be strongly associated with reduced smoking cessation in cancer patients at any time after diagnosis (aOR=4.28, 95% CI (1.56-11.78), P=4.8E-3), while there was a less strong and non-significant trend for home exposure to SHS and reduced smoking cessation at 1 year after diagnosis (aOR=2.56, 95% CI (0.91-7.22), P=0.08)). Time-to-quitting analysis for home exposure to SHS were consistent with these results (aHR=2.76, 95% CI (1.15-6.59), P=0.02)). Unlike lung and head and neck cancer patients, spousal and peer smoking were not found significantly associated with smoking cessation at either time-point (P>0.05). Kaplan-Meier analysis found that 72% of patients who quit, did so within 1 year of their cancer diagnosis. When comparing factors between patients quitting one year after diagnosis versus quitting more than one year after diagnosis, those quitting at one year were more likely older (P<0.05) and have received surgery as part of their cancer care (P=0.06). Conclusions: Home exposure to SHS is a significant barrier to quitting smoking after a diagnosis of cancer in both tobacco-related and non-tobacco related cancers; while spousal and peer smoking were not found significantly associated with smoking cessation in non-tobacco related cancers. Unlike in tobacco-related cancers, home exposure to SHS had a weaker association with quitting at 1 year after diagnosis than quitting at any time after diagnosis; suggesting the effect of the “teachable moment” with SHS and cancer may not be as strong in these cancers. Survivorship programs focusing on secondary prevention and smoking cessation in cancer patients should focus on incorporating SHS exposure. Citation Format: Lawson Eng, Xin Qiu, Jie Su, M Catherine Brown, Margaret Irwin, Dan Pringle, Hiten Naik, Chongya Niu, Mary Mahler, Henrique Hon, Kyoko Tiessen, Rebecca Charow, Henry Thai, Valerie Ho, Vivien Pat, Lindsay Herzog, Anthea Ho, Jennifer M. Jones, Doris Howell, David P. Goldstein, Meredith E. Giuliani, Wei Xu, Peter Selby, Geoffrey Liu. Second-hand smoke (SHS) and smoking cessation in non-tobacco related cancers. [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr B15.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.138
GPT teacher head0.447
Teacher spread0.309 · 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".

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Citations0
Published2015
Admission routes2
Has abstractyes

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