Prospective Biopsy-Based Study of CKD of Unknown Etiology in Sri Lanka
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A kidney disease of unknown cause is common in Sri Lanka's lowland (dry) region. Detailed clinical characterizations of patients with biopsy-proven disease are limited, and there is no current consensus on criteria for a noninvasive diagnosis. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We designed a prospective study in a major Sri Lankan hospital servicing endemic areas to ascertain pathologic and clinical characteristics of and assess risk factors for primary tubulointerstitial kidney disease. We used logistic regression to determine whether common clinical characteristics could be used to predict the presence of primary tubulointerstitial kidney disease on kidney biopsy. RESULTS: From 600 new patients presenting to a tertiary nephrology clinic over the course of 1 year, 87 underwent kidney biopsy, and 43 (49%) had a biopsy diagnosis of primary tubulointerstitial kidney disease. On detailed biopsy review, 13 (30%) had evidence of moderate to severe active kidney disease, and six (15%) had evidence of moderate to severe chronic tubulointerstitial kidney disease. Patients with tubulointerstitial kidney disease were exclusively born in endemic provinces; 91% spent a majority of their lifespan there. They were more likely men and farmers (risk ratio, 2.0; 95% confidence interval, 1.2 to 2.9), and they were more likely to have used tobacco (risk ratio, 1.7; 95% confidence interval, 1.0 to 2.3) and well water (risk ratio, 1.5; 95% confidence interval, 1.1 to 2.0). Three clinical characteristics-age, urine dipstick for protein, and serum albumin-could predict likelihood of tubulointerstitial kidney disease on biopsy (model sensitivity of 79% and specificity of 84%). Patients referred for kidney biopsy despite comorbid diabetes or hypertension did not experience lower odds of tubulointerstitial kidney disease. CONCLUSIONS: A primary tubulointerstitial kidney disease occurs commonly in specific regions of Sri Lanka with characteristic environmental and lifestyle exposures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".