Do patient‐reported measures of symptoms and health status predict mortality in hemodialysis? An assessment of POS‐S Renal and EQ‐5D
Bibliographic record
Abstract
Introduction Experience with the use of patient-reported outcome measures such as EQ-5D and the symptom module of the Palliative care Outcome Scale-Renal Version (POS-S Renal) as mortality prediction tools in hemodialysis is limited. Methods A prospective survival study of people receiving hemodialysis (N = 362). The EQ-5D and the POS-S Renal were used to assess symptom burden and self-rated health (with a self-rated component). Participants were followed from instrument completion to death or study end. Competing risks survival analysis was used to evaluate associations with time to death, with renal transplant as a competing risk. Findings 32% (N = 116) of participants died over a median (25th-75th centile) of 2.6 (1.41-3.38) years. Factors most notably associated with mortality adjusted hazard ratio (95%CI) included: lower EQ VAS score 2.7 (1.4, 5.2) P = 0.004 (lowest tertile), higher POS-S Renal score 2.4 (1.3, 4.3) P = 0.004 (highest tertile), and lower EQ-5D score 2.6 (1.3, 5.3) P = 0.01 (lowest tertile) as well as the presence of: "problems with mobility?" 2 (1.1, 3.3) P = 0.01, or "problems with usual activities?" 2.1 (1.4, 3.3), P < 0.001. After age adjustment area under the receiver operating curves (AUC) (95%CI) for mortality were: 0.71 (0.62, 0.79) for EQ VAS score, 0.71 (0.63, 0.80) for POS-S Renal-S Renal score, and 0.76 (0.68, 0.84) for EQ-5D score. AUC 95%CI was highest for our fourth model at 0.79 (0.72, 0.86) comprised of individual elements from both instruments and established risk factors. Discussion EQ VAS scores and predictive models based on combinations of elements from the POS-S Renal and EQ-5D instruments may aid in mortality discrimination and possibly in the delivery of supportive care services.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".