Epidemiology of Septic Acute Kidney Injury
Bibliographic record
Abstract
The incidence of sepsis and acute kidney injury (AKI) are increasing in critically ill patients and both portend a higher risk of morbidity and death. Sepsis has consistently been shown to be a key contributing factor for the development of AKI. Numerous observational studies have found septic AKI to be highly common among the critically ill. Septic AKI patients are characterized by important differences in baseline demographics, acuity of illness and treatment intensity when compared with non-septic AKI. In particular, these patients are often older, have a higher prevalence of co-morbid illnesses, and are admitted for medical or emergency surgical indications. These patients show greater aberrancy in vital signs, laboratory parameters and need for vasoactive therapy and/or mechanical ventilation. Delays in initiation of appropriate antimicrobial therapy independently predict development of AKI in septic patients. Both delays to appropriate antimicrobials and initiation of renal support are also associated with higher mortality. Survival to ICU and/or hospital discharge for septic AKI patients is significantly lower when compared to patients with either non-septic AKI or sepsis alone. However, survivors of septic AKI show trends for greater rates of renal recovery and dialysis independence compared with non-septic AKI. The burden of septic AKI continues to increase and remains associated with an unacceptably high attributable morbidity and mortality. Accordingly, there is continued need to understand its epidemiology, not only to guide in management of these patients at the bedside, but also to stimulate advances in understanding its pathophysiology and in therapeutic interventions to potentially mitigate prognosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".