WCN25-2757 COMPARISON OF BASELINE KIDNEY FUNCTION AND CHANGES IN ESTIMATED GLOMERULAR FILTRATION RATE BEFORE AND DURING COVID-19 PANDEMIC
Bibliographic record
Abstract
categories.The comparison with the Acon analyzer for urine albumin show good accuracy of 87.6% between the two measurement but more variability for urine creatinine with accuracy of 63.8%.Conclusions: This study shows that Neodocs strip with app is an accurate screening tool and can correctly classify the vast majority of patients screened accurately into A1 vs A2 or A3, allowing early referral for those with A2 or A3 for further evaluation and management.This study will need validation with larger cohorts and with measured urine albumin and urine creatinine as well.With its ease of use, being vernacular, ability to avoid need for training of community health care workers, store images of the tests and allow for validation this could be a game changer for community screening of CKD across the world.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".