Collection of daily patient reported outcomes is feasible and demonstrates differential patient experience in chronic kidney disease
Bibliographic record
Abstract
INTRODUCTION: Patient reported outcomes (PROs) are a critical metric documenting the impact of disease and treatment from the patient's perspective. A variety of generic and disease specific PRO measures (PROMs) are used in chronic kidney disease (CKD) but studies are primarily cross-sectional. None of the available PROMs are designed for frequent iterative application. METHODS: An online PROM for daily use in dialysis and CKD 4/5 patients was developed. The custom website utilised visual analogue scales to capture 6 PROs (general well being (GWB), pain, sleep, breathing, energy, and appetite). Outcomes of interest were uptake, response rates, intermodality variation, and change in PRO corresponding to predefined events. FINDINGS: Forty-three patients submitted at least once and 34 submitted beyond 30 days. Median follow-up was 247 days, 64% male, age 62 ± 12 years. In individuals submitting for >30 days, dialysis patients had significantly worse median scores compared to CKD for sleep (47[32-80], 97[76-99], P = 0.003), appetite (66[50-96], 97[88-100], P = 0.008), energy (47[40-89], 84[67-96], P = 0.031), and GWB (63[49-94], 93[71-98], P = 0.026). Patients demonstrated a variety of stable bandwidths of response, deviations from this were associated with specific events e.g., acute admission, vascular procedures, disturbed fluid status, and dialysis start. DISCUSSION: We successfully introduced an online, patient acceptable, iterative PROM that discriminates symptom burden, cross-sectionally, and longitudinally. Further work will prospectively examine the predictive power of changes in PRO and more rigorously investigate the potential use of these methods to optimise patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".