Pathophysiology of septic acute kidney injury: What do we really know?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Septic acute kidney injury accounts for close to 50% of all cases of acute kidney injury in the intensive care unit and, in its various forms, affects between 15% and 20% of intensive care unit patients. However, there is little we really know about its pathophysiology. Although hemodynamic factors might play a role in the loss of glomerular filtration rate, they may not act through the induction of renal ischemia. Septic acute renal failure may, at least in patients with a hyperdynamic circulation, represent a unique form of acute renal failure: hyperemic acute renal failure. Measurements of renal blood flow in septic humans are now needed to resolve this pivotal pathophysiological question. Whatever may happen to renal blood flow during septic acute kidney injury in humans, the evidence available suggests that urinalysis fails to provide useful diagnostic or prognostic information in this setting. In addition, nonhemodynamic mechanisms of cell injury are likely to be at work. These mechanisms are likely due to a combination of immunologic, toxic, and inflammatory factors that may affect the microvasculature and the tubular cells. Among these mechanisms, apoptosis may turn out to be important. It is possible that, as evidence accumulates, the paradigms currently used to explain acute renal failure in sepsis will shift from ischemia and vasoconstriction to hyperemia and vasodilation and from acute tubular necrosis to acute tubular apoptosis or simply tubular cell dysfunction or exfoliation. If this were to happen, our therapeutic approaches would also be profoundly altered.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it