Impact of Expanded Hemodialysis Using Medium Cut-off Dialyzer on Quality of Life: Application of Dynamic Patient-Reported Outcome Measurement Tool
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Current hemodialysis (HD) treatments have limited ability to clear larger-molecular-weight uremic toxins. Retention is associated with increased symptom burden, low health-related quality of life (HRQoL), and high mortality. Improved clearance, using novel medium cut-off dialyzers, termed expanded HD (HDx), may be associated with improved subjective experience. We have previously developed a dynamic patient-reported outcome measure (PROM) instrument to allow iterative recording to better appreciate the overall burden of disease and assess the impact of therapy changes. STUDY DESIGN: Single-center interventional pilot study. SETTING & PARTICIPANTS: 28 patients established on maintenance HD, London, Ontario, Canada. INTERVENTION: Initial study consisting of 2-week observation (baseline-conventional high-flux HD) followed by 12 weeks of HDx. HRQoL was assessed using the dynamic PROM instrument thrice weekly (enabled in a dedicated app as the London Evaluation of Illness [LEVIL]). Extension phase; 2-week baseline with 24 weeks of HDx and 8-week washout. OUTCOMES: Principal aim was to establish whether HDx therapy was associated with improved HRQoL, evidence of dose-dependant response, and whether effects were durable over time, using LEVIL. RESULTS: Patients with lower LEVIL scores (<70/100) at baseline showed improvement in overall HRQoL after 8 weeks of therapy with similar carryover effect. General well-being, energy, and sleep quality were improved significantly as a consequence of HDx therapy. There were no detrimental effects of HDx detected in patients with higher baseline HRQoL. LIMITATIONS: Small nonrandomized sample size. The coronavirus disease 2019 pandemic interfered with the extension phase. CONCLUSIONS: Dynamic PROM assessment effectively identified patients with lower HRQoL and higher symptom burden, demonstrating durable time/dose-dependent improvements across a range of symptom domains. The use of this instrument may allow targeted selection of patients most likely to benefit from HDx therapy and assist in monitoring response and defining effect size and treatment duration to allow optimal design of further definitive randomized controlled trials of this newly introduced technology. FUNDING: Baxter Healthcare Canada. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT03640858.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".