The risk of mycobacterial infections associated with inhaled corticosteroid use
Bibliographic record
Abstract
Inhaled corticosteroid (ICS) use is associated with an increased risk of pneumonia. This study was performed to determine if ICS use is associated with an increased risk of nontuberculous mycobacterial pulmonary disease (NTM-PD) or tuberculosis (TB). We conducted a population-based nested case–control study using linked laboratory and health administrative databases in Ontario, Canada, including adults aged ≥66 years with treated obstructive lung disease ( i.e. asthma, chronic obstructive pulmonary disease (COPD) or asthma–COPD overlap syndrome) between 2001 and 2013. We estimated odds ratios comparing ICS use with nonuse among NTM-PD and TB cases and controls using conditional logistic regression. Among 417 494 older adults with treated obstructive lung disease, we identified 2966 cases of NTM-PD and 327 cases of TB. Current ICS use was associated with NTM-PD compared with nonuse (adjusted OR (aOR) 1.86, 95% CI 1.60–2.15) and was statistically significant for fluticasone (aOR 2.09, 95% CI 1.80–2.43), but not for budesonide (aOR 1.19, 95% CI 0.97–1.45). There was a strong dose–response relationship between incident NTM-PD and cumulative ICS dose over 1 year. There was no significant association between current ICS use and TB (aOR 1.43, 95% CI 0.95–2.16). This study suggests that ICS use is associated with an increased risk of NTM-PD, but not TB.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".