Qualities of fatigue in patients on chronic hemodialysis
Bibliographic record
Abstract
We aimed to assess the relationship among fatigue qualities (FQ) and the association of FQ with various characteristics of chronic hemodialysis (HD) patients. In 68 HD patients, we assessed the Charlson Comorbidity Index (CCI), the Geriatric Depression Scale score (GDS), the Mini Mental Status Examination (MMSE), and measured the laboratory parameters. In addition, patients answered to six questions about FQ (Tiredness: Do you feel tired much of the time? Emotional: Do you feel that life is empty? Cognitive: Do you have trouble concentrating? Sleepiness: Have you had difficulty sleeping in the past month? Weakness: Have you had muscle weakness in the past month? Lack of energy: Do you feel full of energy?). At least one FQ was reported by 62 patients. Muscle weakness (61.7%) was the most frequent and cognitive fatigue (22%) the least. Physical FQ were all more common than the mental ones. Correlation between the two mental FQ (emotional and cognitive) was 0.381 (p = 0.002). Six patients reported none of the FQ, 20 one FQ, 13 two FQ, and 29 three or more FQ. CCI and GDS were associated with all FQ and MMSE with all FQ but sleepiness. Patients reporting ≥3 FQ were older, had more comorbidities, more symptoms of depression, and a lower MMSE score. At multivariate linear regression analysis, the GDS was the only significant predictor of the number of FQ. HD patients report a variety of qualities of fatigue and the number of FQ is independently associated with symptoms of depression.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".