FP747TELEHEALTH TECHNOLOGY: A PATIENT CENTRED INTERVENTION IN PERITONEAL DIALYSIS
Bibliographic record
Abstract
Introduction and Aims: Introduction: Long term conditions form a major part of the total health care provision in the developed world. The introduction of telehealth in many chronic conditions has improved quality of life and well being of patients. This type of approach has not been extensively utilised in patients with end stage renal disease (ESRD). Aim: The aim of this pilot study is to explore the role of telehealth technologies in self-management of patients with ESRD on peritoneal dialysis and assess user satisfaction by ‘Quebec User Evaluation of Satisfaction with assistive Technology' (QUEST Version 2.0) questionnaire.(1) Methods: We have developed a computer tablet (POD) based technology with specialised software that enables remote monitoring, communication and continuous learning in patients with end stage renal disease (ESRD). It allows early detection of medical problems; dietary issues and intolerances to medications amongst others. The PODs are a single platform for integrated care between hospitals and patients. It sends vital data including weight and blood pressure to a clinical user interface (CUI) through Bluetooth integration with peripheral devices. There are resources, both web based, and information sheets, and access to ‘Renal Patient View’ a portal that provides patients test results and medication details. The software contains a questionnaire, QUEST Version 2 to assess user satisfaction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".