Patient-reported outcome measures in the care of in-centre hemodialysis patients
Bibliographic record
Abstract
Kidney failure requiring dialysis is associated with high symptom burden and low health-related quality of life (HRQL). Patient-reported outcome measures (PROMs) are standardized instruments that capture patients' symptom burden, level of functioning, and HRQL. The routine use of PROMs can be used to monitor aspects of patients' health that may otherwise be overlooked, inform care planning, and facilitate the introduction of treatments. Incorporating PROMs into clinical practice is an appropriate strategy to engage patients and enhance their role in decisions regarding their care and outcomes. However, the implementation of PROMs measurement and associated interventions can be challenging given the nature of clinical practice in busy hemodialysis units, the variations in organization and clinical workflow across units, as well as regional programs. Implementing PROMs and linking these with actionable treatment aids to alleviate bothersome symptoms and improve patients' wellbeing is key to improving patients' health. Other considerations in implementing PROMs within a hemodialysis setting include integration into electronic medical records, purchase and configuration of electronic tools (i.e., tablets), storage and disinfection of such tools, and ongoing IT resources. It is important to train clinicians on the practical elements of using PROMs, however there is also a need to engage clinicians to use PROMs on an ongoing basis. This article describes how PROMs have been implemented at in-centre hemodialysis units in Alberta, Canada, addressing each of these elements.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".