Evaluating the risk of pneumonia with inhaled corticosteroids in COPD: Retrospective database studies have their limitations SA
Bibliographic record
Abstract
An increased risk of non-fatal pneumonia has been documented in COPD patients treated with inhaled corticosteroids (ICS) in randomized clinical trials. Retrospective database analyses have been conducted to evaluate this signal in larger populations treated in the community. To understand how methodological choices may influence results in observational studies, we compared two recent Canadian studies which used health administrative databases from Quebec and Ontario and came to opposite conclusions on the risk of pneumonia in ICS treated COPD patients. Explanations for why the results of these studies diverged are explored. The Suissa analysis used RAMQ data from Quebec and showed an increased relative risk of serious pneumonia for current users of ICS compared to non users, RR = 1.69 (95% confidence interval, 1.63-1.75). The Gershon analysis used ODB data and showed no difference for pneumonia hospitalisation, RR = 1.01 (0.93-1.08). Reasons for differences in study findings include lack of validated definitions of COPD, poor selection of relevant exposure groups, channeling and confounding biases, and failure to perform on-treatment analyses for safety. CONCLUSION: Our study identifies methodological features that need consideration to increase robustness and minimize threats to internal validity of retrospective health administrative database studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.181 | 0.413 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".