Non‐steroidal Anti‐inflammatory Drugs and the Risk of Pneumonia Complications: A Systematic Review
Bibliographic record
Abstract
There have been concerns regarding the safety of nonsteroidal antiinflammatory drugs (NSAIDs) in patients with respiratory infections. However, to date, the quality of the evidence has not been systematically assessed. The purpose of this systematic review was to evaluate the role of NSAIDs on pneumonia complications. OVID MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects, and Google Scholar were searched. Studies that examined pneumonia complications in patients who had taken NSAIDs before onset of symptoms were identified. Quality assessment was conducted using the Risk of Bias in Non-randomized Studies - of Interventions (ROBINS-I) assessment tool, which was adapted to include biases that were pertinent to this question. The search strategy identified 1721 potential studies through the 5 primary databases and searching reference lists. Of these, 10 studies met the inclusion criteria, including 5 nested case-control studies, 2 population-based case-control studies, and 3 cohort studies. In total, 59,724 adults were included from 4 of the studies (range = 57-59,250) and 1217 children from 5 studies (range = 148-540). All studies demonstrated a positive association; in adults (odds ratio/risk ratio range = 1.8-8.1) and children (odds ratio/risk ratio range = 1.9-6.8). Studies were limited by moderate or serious risk of confounding bias, exposure misclassification, and protopathic biases and sparse data bias. The results of this review demonstrate that published studies on the effect of NSAIDs use and risk of pneumonia complications are subject to a number of biases. These results should not be extrapolated as evidence of harm for NSAIDs, including ibuprofen, in respiratory ailments but highlight the need for more methodologically robust studies to evaluate this potential relationship.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".