Asthma severity as a contributing factor to cancer incidence: A cohort study
Bibliographic record
Abstract
BACKGROUND: A putative link between asthma and asthma severity with the occurrence of cancer has been suggested but has not been fully investigated. The objective of this study is to assess the incidence of all types of cancer in a cohort of asthmatic patients. METHODS AND FINDINGS: A single center cohort retrospective study was conducted to investigate the role of asthma as a potential risk factor for various cancers. Participants were followed for a period of 9 years from 01/01/2010 to 30/12/2018 and cancer incidence and its determinants were collected in asthmatic patients and controls from the same population source but without any respiratory disease. Overall, 2,027 asthma patients and 1,637 controls were followed up for an average of 9 years. The statistical analysis showed that 2% of asthma patients were diagnosed with various cancers, resulting in an incidence rate of cancer of 383.02 per 100,000 persons per year which is significantly higher than the 139.01 per 100,000 persons per year observed in matched controls (p-value < 0.001). The top four cancers reported among asthmatics were breast, colon, lung and prostate cancer. Lung cancer in asthmatics had the longest diagnosis period with a mean of 36.6 years compared to the shortest with prostate cancer with 16.5 years. CONCLUSIONS: This study shows that asthma patients are at increased risk of different types of cancers with asthma severity and goiter as the main factors that may increase the risk of developing cancers among asthmatic patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".