Kannada Translation and Validation of the ESAS-r Renal for Symptom Burden Survey in Patients with End-Stage Kidney Disease
Bibliographic record
Abstract
Objectives: End-stage kidney disease (ESKD) is a life-limiting illness that leads to significant health-related suffering for the patients and their caregivers. Moreover, disease-directed options such as dialysis and renal transplant might not be universally accessible. Inadequate assessment and management of symptoms often lead to diminished quality of life. For evaluating symptoms and their associated distress, various tools have been identified. However, these are not available for the native Kannada-speaking population for assessing ESKD symptom burden. In this study, we determined the reliability and validity of the Edmonton Symptom Assessment System Revised Renal (ESAS-r: Renal) in Kannada-speaking ESKD patients. Materials and Methods: ESAS-r: Renal English version was translated into Kannada using the forward and backward method. The translated version was endorsed by Nephrology, Palliative care, Dialysis technology and Nursing experts. As a pilot study, 12 ESKD patients evaluated the content of the questionnaires for appropriateness and relevance. The ESAS-r: Renal Kannada version was validated by administering this tool to 45 patients twice a fortnight. Result: The translated ESAS-r: Renal Kannada version questionnaire had an acceptable face and content validity. Experts' opinion was assessed by content validity ratio (CVR), and the value of CVR of ESAS-r: Renal Kannada version was-'1'-. Internal consistency of the tool was assessed among Kannada-speaking ESKD patients; its Cronbach's α was 0.785, and test-retest validity was 0.896. Conclusion: The validated Kannada version of ESAS-r: Renal was reliable and valid for assessing symptom burden in ESKD patients.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".