Letter to the Editor
Bibliographic record
Abstract
Background: Hypoxia is a key driver of fibrosis and is associated with capillary rarefaction in humans.Objectives: Characterize capillary rarefaction in cats with chronic kidney disease (CKD).Animals: Archival kidney tissue from 58 cats with CKD, 20 unaffected cats.Methods: Cross-sectional study of paraffin-embedded kidney tissue utilizing CD31 immunohistochemistry to highlight vascular structures.Consecutive high-power fields from the cortex (10) and corticomedullary junction (5) were digitally photographed.An observer counted and colored the capillary area.Image analysis was used to determine the capillary number, average capillary size, and average percent capillary area in the cortex and corticomedullary junction.Histologic scoring was performed by a pathologist masked to clinical data.Results: Percent capillary area (cortex) was significantly lower in CKD (median 3.2, range, 0.8-5.6)compared to unaffected cats (4.4,1.8-7.0;P = <.001) and was negatively correlated with serum creatinine concentrations (r = À.36,P = .0013),glomerulosclerosis (r = À0.39,P = <.001),inflammation (r = À.30,P = .009),and fibrosis (r = À.30,P = .007).Capillary size (cortex) was significantly lower in CKD cats (2591 pixels, 1184-7289) compared to unaffected cats (4523 pixels, 1801-7618; P = <.001) and was negatively correlated with serum creatinine concentrations (r = À.40,P = <.001),glomerulosclerosis (r = À.44,P < .001),inflammation (r = À.42,P = <.001), and fibrosis (r = À.38,P = <.001).Conclusions and Clinical Importance: Capillary rarefaction (decrease in capillary size and percent capillary area) is present in kidneys of cats with CKD and is positively correlated with renal dysfunction and histopathologic lesions.
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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 | 0.004 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.034 | 0.026 |
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".