Bibliographic record
Abstract
Advances in the use of ultrasonography can enhance our ability to better characterize acute kidney injury (AKI). AKI is a clinical syndrome characterized by a rapid decrease in kidney excretory function with the accumulation of products of nitrogen metabolism and other clinically unmeasured waste products, and may proceed to include clinical manifestations including decreased urine output, development of metabolic acidosis, and electrolyte abnormalities [1]. The Kidney Disease Improving Global Outcomes (KDIGO) criteria defines AKI (Table 1). Staging severity of AKI guides the physician in respect to medical management and prognosis. The overall incidence of AKI is around 20% of patients hospitalized worldwide, and around 50% in intensive care unit (ICU) patients [2, 3]. AKI has been found to have increasing morbidity and mortality, no matter the cause of admission, as well as an in-hospital mortality of close to 50% [4]. In a large study of 8 ICUs over 8 years, Kellum et al. found that AKI was associated with increasing mortality rate with worsening AKI stage. A decrease in urine output alone, without an increase in serum creatinine, was associated with decreased 1-year survival [5]. Recurrent AKI has also been associated with increased mortality, further demonstrating the importance of detecting, monitoring, and diagnosing AKI [6].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".