Bibliographic record
Abstract
[ ]urine albumin to creatinine ratio is often measured at the point-of-care in a network of clinics Because of limited data on the quality of this test, Tamika Regnier, BMedSc, and colleagues investigate this aspect using 16 years of analytical quality data Adil I Khan, MSc, PhD Adil I Khan, MSc, PhD, is an associate professor of pathology at the Lewis Katz School of Medicine, Temple University, in Philadelphia, Pennsylvania, and the medical director for point-of-care testing and clinical chemistry for the Temple University Health System Dr Khan then pursued a postdoctoral research fellowship at the University of Calgary, Canada, studying the role of L-selectin and CD44 in models of acute inflammation, followed by a postdoctoral clinical chemistry training fellowship at the University of Texas Southwestern Medical Center at Dallas Since 2006, he has been teaching at Temple University, lecturing to pathology residents, medical students, and students of podiatric medicine
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.077 | 0.025 |
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".