Sleep and pain management are key components of patient care in ESRD
Bibliographic record
Abstract
The care for patients with end-stage renal disease (ESRD) has focused on easily measurable processes of care outcomes such as Kt/V, hemoglobin and serum phosphorus levels. It has been thought that these metrics reflect the quality of care. Furthermore, improving these measurements would favorably influence the quality of life and survival on dialysis. However, an observational study of over 11 000 hemodialysis patients demonstrated no substantial improvement in health-related quality of life (HRQOL), despite secular changes in Kt/V, hemoglobin and serum phosphorus [1]. In addition, randomized trials testing whether increasing Kt/V or hemoglobin reduces mortality and improves the quality of life had demonstrated no substantial increase in quality or length of life [2–4]. As it turns out, these measures may not be adequate proxies for patient well-being and attention to them may not substantially increase survival. The present study by Kimmel and colleagues [5] is noteworthy because of its examination of potential associations between pain, sleep, quality of life and survival. The work of this group supports the position that patient-reported outcomes may present an important tool to improve the quality of life and the survival duration of patients with ESRD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".