<scp>SGLT</scp>‐2 inhibitors and the risk of hospitalization for <scp>community‐acquired</scp> pneumonia: A population‐based cohort study
Bibliographic record
Abstract
PURPOSE: Sodium-glucose co-transporter 2 inhibitors (SGLT-2i) have been associated with an increased risk of genitourinary tract infections. Through similar biological mechanisms, they may also increase the risk of community-acquired pneumonia. Our objective was to compare the rate of hospitalization for community-acquired pneumonia (HCAP) with SGLT-2i compared to dipeptidyl peptidase-4 inhibitors (DPP-4i) among patients with type 2 diabetes. METHODS: We used the United Kingdom's Clinical Practice Research Datalink Gold, linked to hospitalization data, to construct a cohort of patients with type 2 diabetes. Using a time-dependent Cox proportional hazards model, we estimated the adjusted hazard ratio (HR) for HCAP with current use of SGLT-2i versus DPP-4i. RESULTS: Among 29 896 patients, 705 HCAPs occurred over a mean follow-up of 1.7 years (SD: 1.2). Incidence rates for SGLT-2i and DPP-4i users were 6.2 (95% confidence interval [CI]: 3.7, 10.2) and 17.8 (95% CI: 15.3, 20.7) per 1000 person-years, respectively. Current use of SGLT-2i was associated with a decreased risk of HCAP compared to current use of DPP-4i (adjusted HR: 0.48, 95% CI: 0.28, 0.82). However, a comparison of SGLT-2i versus glucagon-like peptide-1 receptor agonists (GLP-1 RA) found no difference in risk of HCAP (adjusted HR: 0.94, 95% CI: 0.44, 1.89). CONCLUSIONS: SGLT-2i are associated with a decreased rate of HCAP compared to DPP-4i, but not when compared to GLP-1 RA, among patients with type 2 diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".