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Record W2068873298 · doi:10.1158/1538-7445.am2013-3633

Abstract 3633: Cannabis smoking and lung cancer risk: pooled analysis in the International Lung Cancer Consortium.

2013· article· en· W2068873298 on OpenAlexaffabout
Li Zhang, Zuo‐Feng Zhang, Hal Morgenstern, Shen‐Chih Chang, Philip Lazarus, M. Dawn Teare, Penella J. Woll, Irene Orlow, Brian Cox, Geoffrey Liu

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsOntario Institute for Cancer ResearchLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
Fundersnot available
KeywordsLung cancerMedicineOdds ratioConfidence intervalConfoundingTobacco smokeCannabisCancerRisk factorInternal medicineOncologyLogistic regressionEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Lung cancer remains the leading cause of cancer death worldwide, with tobacco smoking established as the main risk factor. Cannabis smoke contains similar carcinogens as tobacco smoke including the polycyclic aromatic hydrocarbons; animal studies and human case series and histopathologic studies have suggested its potential carcinogenic effect in lungs. However, epidemiologic evidence is limited and conflicting. The present study aimed to examine the role of cannabis smoking in lung cancer risk using a pooled analysis in the International Lung Cancer Consortium (ILCCO). Methods Cannabis smoking and putative lung cancer risk factor data on 2131 lung cancer cases and 3075 controls were harmonized and pooled from six case-control studies in US, Canada, UK and New Zealand within the ILCCO. To standardize the definition and to distinguish occasional/non-users from habitual users, cumulative consumption of 1 joint-year (1 joint-equivalent per day for 1 year) or more was used to define habitual vs. non-users. The association between cannabis smoking (habitual vs. non-users, joint-equivalent per day, duration, and total joint-years) and the risk of lung cancer was assessed by odds ratios (OR) and 95% confidence intervals (CI) obtained from unconditional logistic regression in each study, while adjusting for age, sex, sociodemographic factors and tobacco packyears. Pooled risk estimates were calculated using random effect models. To minimize confounding by tobacco smoking, we also conducted analyses restricted to 367 case and 1400 control never tobacco smokers. Results The summary OR from the six studies for habitual vs. non-users was 1.15 (95% CI: 0.73-1.82, p for heterogeneity: 0.05). Compared to non-users, the summary OR was 1.28 (95%CI: 0.62-2.63) for individuals who smoked cannabis for 20 years or more and 1.53 (95%CI: 0.57-4.09) for those with 10 joint-years or more cumulative consumption. A lack of significant association between cannabis smoking and lung cancer was also observed in the never tobacco smokers: compared to non-users, the OR was 0.99 (95% CI: 0.49-2.00) for habitual users and 2.13 (95%CI: 0.67-6.78) for those who used 20 years or more. Conclusion Our pooled results showed no significant association between the intensity, duration, or cumulative consumption of cannabis smoke and the risk of lung cancer overall or in never smokers. Cannabis use is under international control and its legal status varies, so reporting bias is of concern. However, since the reported prevalence in our data is comparable to nation-specific survey results and not differential between cases and controls, it is unlikely to fully explain the lack of significant association. Our results cannot preclude the possibility that cannabis may exhibit an association with lung cancer risk at extremely high dosage. We will also present data after applying restricted cubic splines to explore non-linear relationships. Citation Format: Li Rita Zhang, Zuo-Feng Zhang, Hal Morgenstern, Shen-Chih Chang, Philip Lazarus, M. Dawn Teare, Penella J. Woll, Irene Orlow, Brian Cox, Geoffrey Liu, Rayjean J. Hung. Cannabis smoking and lung cancer risk: pooled analysis in the International Lung Cancer Consortium. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3633. doi:10.1158/1538-7445.AM2013-3633

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 imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.027
Bibliometrics0.0080.011
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.408
Teacher spread0.376 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations2
Published2013
Admission routes2
Has abstractyes

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