Bibliographic record
Abstract
We know what you're thinking.we've heard it a thousand times: "Oh, you're a kidney doctor who dinks around with ultrasound?What do you look for?Hydronephrosis?"You may be asking, "Is this issue just going to be a bunch of pictures of hydronephrosis and distended bladders?"And yes, for the thousandth time, in acute kidney injury it's almost never wrong to get a kidney and bladder ultrasound as part of the initial workup.But there's so much more!Point of care ultrasound is an incredibly valuable tool to the nephrologist, not just for quickly assessing for urinary outflow obstruction, but for overall assessment of physiology, particularly for volume status assessment.Our raison d'être is building the evidence for point of care ultrasound in nephrology and to that end we present for your approval The POCUS Journal: Kidney Edition.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".