Identifying dimensions of fatigue in haemodialysis important to patients, caregivers and health professionals: An international survey
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures of fatigue used in research in haemodialysis vary widely in the dimensions assessed; and the importance of these dimensions to patients and health professionals is unknown. This study aimed to identify the most important dimensions of fatigue to assess in patients on haemodialysis participating in trials. METHODS: In an international survey, patients/caregivers and health professionals rated the absolute and relative importance of content and measurement dimensions to include in a core outcome measure of fatigue. A 9-point Likert scale (7-9 indicating critical importance) was used to assess absolute importance and best-worst scale was used to assess importance of each dimension compared to others. RESULTS: In total, 169 patients/caregivers and 336 health professionals from 60 countries completed the survey. Both groups (patients/caregivers and health professionals) rated life participation (7.55), tiredness (7.40), level of energy (7.37), ability to think clearly (7.15), post-dialysis fatigue (7.13), motivation (7.03) and ability to concentrate (7.03) as critically important (mean Likert score greater than 7) content dimensions to include in a core outcome measure. Compared to patients and caregivers, health professionals rated post-dialysis fatigue, memory and verbal abilities more highly. Based on the relative importance scores, life participation was ranked most highly above all content dimensions. Severity was rated and ranked the most important measurement dimension by all stakeholders. CONCLUSION: A core outcome measure of fatigue should assess impact of fatigue on life participation, tiredness and level of energy, using a severity scale. A consistent and valid measurement of fatigue will improve the value of trials in supporting decision-making based on this important outcome.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".