Bibliographic record
Abstract
Acute kidney injury (AKI) is a significant problem for patients admitted to the intensive care unit (ICU), both due to the high incidence and associated mortality with rates of AKI requiring renal replacement therapy (RRT) of over 5%, and mortality rates with AKI of over 60% 1, 2.Ultrasound can be used to identify those at risk for AKI and assist with AKI management. Risk factors for AKI in the ICU not only include hypoperfusion but also venous congestion and volume overload. Volume overload and vascular congestion are associated with multi-organ dysfunction and worse renal outcomes. Daily and overall fluid balance, daily weights, and physical examination for edema can be inaccurate and belie true systemic venous pressure 3, 4, 5. Bedside ultrasound allows providers to evaluate vascular flow patterns and obtain a more reliable evaluation of volume status to guide and individualize therapies. Cardiac, lung, and vascular patterns on ultrasound can identify preload responsiveness, which should be assessed to safely manage ongoing fluid resuscitation and assess for signs of fluid intolerance. Here we present an overview in the use of point of care ultrasound with particular emphasis on nephro-centric strategies, namely in the identification of the type of renal injury, renal vascular flow assessment, the static measure of volume status, as well as dynamic evaluation for volume optimization in critically ill patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".