Long-term risk of sepsis among survivors of acute kidney injury
Bibliographic record
Abstract
Many prior studies have shown that, in critically ill patients, acute kidney injury (AKI) commonly occurs in association with sepsis and its presence portends an increased likelihood of poor outcomes. In contrast, few studies have focused specifically on the influence of AKI on the long-term risk of developing sepsis. In a previous issue of Critical Care, a population-based cohort study by Lai and colleagues reported a long-term increased risk of severe sepsis for patients surviving beyond 90 days following hospitalization with an episode of AKI requiring renal replacement therapy. While the pathophysiologic mechanisms that underpin this finding remain to be elucidated and causality cannot be proven, this study suggests that severe AKI confers long-term susceptibility to infection and focuses further attention on the critical importance of long-term surveillance for survivors of severe AKI. Further mechanistic and clinical studies are required to more precisely define the extent and duration of any increased risk of severe sepsis beyond which a need for ongoing renal replacement therapy following AKI might be associated. Nonetheless, this novel study by Lai and colleagues could lead to a number of important new avenues for clinical inquiry, such as whether it might be possible to identify those most susceptible to severe sepsis after AKI and, ultimately, whether such episodes might be preventable.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".