Global Burden and Risk of Upper Gastrointestinal Cancer Associated with Smoking Across Histological Types and Subsites: A Population-Based Study, Systematic Review, and Meta-Analysis
Bibliographic record
Abstract
eview question / Objective To elucidate the impact of smoking on diverse subtypes and their respective trends.Condition being studied Esophageal and gastric cancer, which are refered as upper gastrointestinal cancer. METHODSParticipant or population Cancer patients and controlled population, with or without smoking exposure(current or former). Intervention No intervention is involved.Comparator No comparator is involved.Contolled group is defined as population without smoking history. Study designs to be included Case control studies and cohort studies.Eligibility criteria Inclusion criteria: 1) The smoking status was ascertained, and the association between smoking and cancer risk was quantified using odds ratios (ORs), risk ratios (RRs), hazard ratios (HRs), or a format enabling estimation of relative risk.; 2) case-control studies or cohort studies; and 3) If multiple reports on the same study population existed, only the most updated report with the longest follow-up period was included.Exclusion criteria were set as 1) Studies that included data from specific study populations (e.g., pregnant, hereditary carcinoma, or patients with other specific chronic diseases (like comorbidity with DM) solely; 2) Studies exclusively conducted in cancer patients who had undergone surgery or chemoradiotherapy; 3) Not using non-smoker as the reference; 4) Special study types: reviews or systematic reviews, conference papers, editorials, case reports, and animal experimental studies.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".