Asthma and asthma symptom control in relation to incidence of lung cancer in the HUNT study
Bibliographic record
Abstract
Large prospective studies on asthma, especially asthma symptom control, as a potential risk factor for lung cancer are limited. We followed up 62,791 cancer-free Norwegian adults from 1995-1997 to 2017. Self-reported doctor-diagnosed asthma was categorized into active and non-active asthma. Levels of asthma symptom control were classified into controlled and partially controlled (including partly controlled and uncontrolled) according to the Global Initiative for Asthma guidelines. Incident lung cancer cases were ascertained from the Cancer Registry of Norway. Cox regression models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for possible associations. Totally, 984 participants developed lung cancer during a median follow-up of 21.1 years. After adjustment for smoking and other potential confounders, an increased incidence of lung cancer was found for adults with partially controlled asthma (HR 1.39, 95% CI 1.00-1.92) compared with those without asthma at baseline. Adults with active asthma had a tendency of increased lung cancer incidence (HR 1.29, 95% CI 0.95-1.75). Sensitivity analyses indicated that the observed associations were less likely resulted from reverse causation or residual confounding by smoking. Our findings suggested that proper control of asthma symptoms might contribute to a reduced incidence of lung cancer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".