Assessment of Fatigue in Patients with End-stage Kidney Disease: Validation of PROMIS Fatigue Computer Adaptive Test and Identifying Correlates of Fatigue in Patients with End-stage Kdney Disease
Bibliographic record
Abstract
Fatigue is a common and debilitating symptom in patients with end-stage kidney disease (ESKD). The routine assessment of fatigue with patient-reported outcome measures can help clinicians identify and manage fatigue efficiently. In this study, the measurement properties of the Patient-Reported Outcomes Measurement Information System Fatigue Computer Adaptive Test (PROMIS Fatigue CAT) were assessed in patients with ESKD. A cross-sectional sample of adult patients on dialysis and kidney transplant recipients completed the PROMIS Fatigue CAT as well as a legacy instrument. PROMIS Fatigue CAT demonstrated excellent reliability and validity. Excellent discrimination was also found for PROMIS Fatigue CAT to discriminate patients with fatigue. In addition, sex, depression, anxiety, social support and self-reported health status were strongly associated with fatigue. Thus, PROMIS Fatigue CAT may be a useful measure of fatigue and psychosocial interventions may be important to consider in the management of fatigue in patients with ESKD.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".