MULTIPLE PRIMARY CANCERS IN LUNG CANCER PATIENTS IN ATLANTIC CANADA AND IMPLICATIONS FOR SCREENING AND MANAGEMENT
Bibliographic record
Abstract
Abstract Background Lung cancer incidence rates in Atlantic Canada significantly exceed those of other Canadian provinces, with underlying causes remaining poorly understood. This regional disparity suggests potential genetic or environmental factors unique to Atlantic Canada. Here we present for the first-time data indicating that accompanying the high lung cancer rates in Atlantic Canada is an intricate landscape of multiple malignancies. This represents a phenomenon of multiple primary cancers that is unprecedented in the literature and presents unique challenges in the diagnosis and treatment of individuals diagnosed with lung cancer in Atlantic Canada. Methods We performed a retrospective chart review of 1,151 patients referred to the thoracic surgeons at the Queen Elizabeth II Health Sciences Centre between 2019 and 2023 with the new diagnosis of lung cancer. The primary focus was to assess the incidence of multiple primary cancers (as documented in pathology reports) and analyze demographic, clinical, and geographical factors through Fisher’s exact tests and Student’s t-tests. Results Forty-three precent (43.3%) of Atlantic Canadian patients presented with multiple primary cancers. Of 1,949 cancers identified, 1,440 were of primary lung origin. Sixty percent (60.0%) of our cohort was female. Thirty-one percent (31.3%) were self-reported current tobacco smokers, 50.3% were former tobacco smokers, and 18.4% reported no tobacco use. The average age and body mass index (BMI) at first diagnosis was similar between those patients with and without multiple primary cancers, averaging 67 years and 27.7 kg/m 2 , respectively. Thirty-nine percent (39.2%) of our study participants lived in more densely populated centers (>100,000 people), 10.2% in intermediate centers (∼98,000 people) and 50.7% in more sparsely populated centers (<55,000 people). Conclusions We have uncovered an unprecedented phenomenon in Atlantic Canada of a cohort of patients with lung cancer having multiple primary cancers of different tissue origin at a rate that is four times greater than previously described. The implications of this study are that Atlantic Canadians are disproportionately burdened with cancers of multiple origins that are not explained by smoking rates or BMI. Future studies will investigate the contribution of genetic predisposition, environmental exposures, and socioeconomic factors unique to Atlantic Canada underlying the high rates of multiple primary cancers documented here in a lung cancer cohort. Such studies will be essential to develop effective cancer prevention and screening strategies in Atlantic Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".