Additional file 1 of Body mass index and incidence of lung cancer in the HUNT study: using observational and Mendelian randomization approaches
Bibliographic record
Abstract
Additional file 1: Supplementary Table S1. The association of BMI in HUNT2 with incidence of lung cancer overall and different histologic types after excluding the first five years’ follow-up, the HUNT Study, 1995-97 to2017 (N = 59,711). Supplementary Table S2. The associations of BMI in HUNT2 with lung cancer incidence taking account of competing risk due to death, the HUNT Study, 1995-97 to 2017 (N = 62,453). Supplementary Text 1. Analysis using negative control exposure. Supplementary Figure 1. DAG for body mass index(BMI) (as the main exposure), migraine (as the negative control exposure) and incidence of lung cancer (as the outcome). Supplementary Table S3. Negative control using migraine as an alternative exposure to address residual confounding by smoking for the association of BMI in HUNT2 with lung cancer incidence across different lung cancer subtypes, the HUNT Study, 1995-97 to 2017(N = 49,969). Supplementary Table S4. Comparison of baseline characteristics of participants with complete and missing information on 75 BMI SNPs in HUNT2. Supplementary Text 2. Univariable Mendelian randomization (MR) analyses using the 61 single-nucleotide polymorphisms only for body mass index (BMI-Only SNPs). Supplementary Table S5. Associations of externally weighted BMI GRS1 based on 61 BMI-Only SNPs with potential confounders in HUNT2, 1995-1997 (N = 54,511). Supplementary Table S6. The association of BMI with incidence of lung cancer overall and different histologic types based on the univariable MR analyses using 61 BMI-Only SNPs,the HUNT Study, 1995-97 to 2017 (N = 54,511).
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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 | 0.005 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.769 | 0.067 |
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".