<i>N</i> -Acetyl-β- <scp>d</scp> -Glucosaminidase Index as an Early Biomarker for Chronic Kidney Disease in Cats with Hyperthyroidism
Bibliographic record
Abstract
BACKGROUND: Hyperthyroid cats are at risk of developing azotemic chronic kidney disease (CKD) and diagnostic tools currently used to screen for CKD in hyperthyroid cats are either unreliable or impractical. HYPOTHESIS: Urine N-acetyl-beta-D-glucosaminidase index (NAG(i)) is a good biomarker for azotemic CKD in hyperthyroid cats. ANIMALS: Twenty-four newly diagnosed nonazotemic hyperthyroid cats and 10 healthy cats. METHODS: All cats were evaluated for hyperthyroidism at baseline. Hyperthyroid cats were treated with methimazole and reevaluated once euthyroid. At the end of the study, cats were divided into 3 groups: healthy cats, nonazotemic, and azotemic euthyroid cats. Baseline group characteristics were compared to predict azotemic CKD. The influence of treatment on NAG(i) was evaluated. RESULTS: Baseline NAG(i) was significantly different among groups (P= .004). Azotemic cats had a higher median value (13.12 U/g) when compared with healthy cats (1.38 U/g). With NAG(i) >2.76 U/g, negative and positive predictive values for development of azotemia were 77.7 and 50%, whereas the combination of a urine specific gravity (USG) <or=1.035 and T(4) >7.80 microg/dL enhanced predictive values to 88.9 and 83.3%, respectively. NAG(i) values decreased significantly over time in treated nonazotemic cats. CONCLUSIONS AND CLINICAL RELEVANCE: Baseline NAG(i) did not differentiate azotemic from nonazotemic euthyroid cats. NAG(i) could be used to assess renal function during medical therapy allowing the clinician to adjust methimazole dosage accordingly. The combination of USG and T(4) could optimize identification of appropriate candidates for permanent treatment of hyperthyroidism.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".