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
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].
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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