Dipeptidyl peptidase‐4 inhibitors and the risk of community‐acquired pneumonia in patients with type 2 diabetes
Bibliographic record
Abstract
AIMS: To determine whether the use of dipeptidyl peptidase-4 (DPP-4) inhibitors is associated with an increased risk of community-acquired pneumonia. METHODS: The UK Clinical Practice Research Datalink and the Hospital Episodes Statistics database were used to conduct a nested case-control analysis within a cohort of new users of antidiabetic drugs between 2007 and 2012. Incident cases of hospitalization for community-acquired pneumonia were matched with up to 20 controls on age, duration of treated diabetes, calendar year and duration of follow-up. Conditional logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for hospitalization for community-acquired pneumonia associated with current use of DPP-4 inhibitors compared with current use of two or more oral antidiabetic drugs. RESULTS: The cohort included 49,653 patients, of whom 562 were hospitalized for community-acquired pneumonia during follow-up (incidence rate 5.2/1000 person-years). Compared with current use of two or more oral antidiabetic drugs, current use of DPP-4 inhibitors was not associated with an increased risk of hospitalized community-acquired pneumonia overall (adjusted OR 0.80, 95% CI 0.50-1.29) or according to duration of use (p for trend = 0.57). CONCLUSIONS: The use of DPP-4 inhibitors was not associated with an increased risk of hospitalization for community-acquired pneumonia. Additional research is needed to assess the association between these drugs and other serious infections.
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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 | 0.004 |
| 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 | 0.001 | 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".