SP502REMOTE MANAGEMENT FOR PERITONEAL DIALYSIS: PERSPECTIVES ON EFFECTS ON CARE
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Remote management (RM) technologies alert and enable health care providers to manage a range of health-related changes remotely. Sharesource is an RM system developed by Baxter International Inc., in use in the United Kingdom (UK) and United States (US) in conjunction with Automated Peritoneal Dialysis (APD) with the potential for improving patient engagement and quality of care. This study aims to identify perceived and anticipated changes in PD care from the perspectives of patients, care partners and healthcare providers (HCPs) due to RM technology. METHODS: We developed semi-structured interview guides in collaboration with a stakeholder panel comprising patients, social workers, nurses and nephrologists. Recruitment efforts targeted patients, care partners and HCPs in the US and only HCPs in the UK due to logistic and regulatory constraints. Participants were recruited through PD clinics, the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS), social media, and online postings. We interviewed 27 participants with ≥3 months of experience with PD, using either conventional or RM systems. Data were entered in Nvivo11 and analyzed to identify PD training and operations that had changed or were perceived as likely to change because of RM. RESULTS: Preliminary findings suggest that both patients and care partners view RM as providing useful information to clinicians with the daily transmission of their treatment information, potentially reducing their own documentation burden, but do not perceive RM as changing their daily management of PD care. Most patients preferred to communicate directly with their nurse by phone if they had any issues with their treatment. Meanwhile HCPs identified changes to PD care procedures that have or might arise due to RM (Table 1). CONCLUSIONS: All stakeholders agreed that RM was a positive improvement for PD. However, among the three stakeholder groups, the current technology may have had the most practical implications in terms of changes to PD care procedures, for HCPS.
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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 | 0.006 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".