Heart Failure and Respiratory Tract Infection: Cause and Consequence of Acute Decompensation?
Bibliographic record
Abstract
This article refers to ‘Empagliflozin and risk of lower respiratory tract infection in heart failure with mildly reduced and preserved ejection fraction: An EMPEROR-Preserved analysis’ by J.P. Ferreira et al., published in this issue on pages 952–959. Heart failure (HF) is a life-threatening syndrome characterized by a high risk of both cardiovascular and non-cardiovascular mortality.1 The burden of comorbidities in patients with HF with mildly reduced ejection fraction (HFmrEF) or preserved ejection fraction (HFpEF)2,3 exposes them to a relevant risk of non-cardiovascular events, including infections. In particular, lower respiratory tract infections (LRTI), such as pneumonia, are common in patients with HF (especially HFpEF), may precipitate HF decompensation events or be a consequence of it and increase the risk of hospitalizations and mortality.4–8 Indeed, infection is an important precipitating factor for decompensation in patients with chronic HF, leading to multiple hospital admissions and worsening of cardiac function, thus fostering transition to advanced HF. On the other hand, congested lung tissue is particularly susceptible for infectious agents and as such decompensation may increase the risk of LRTI.8
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.028 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".