iConnect CKD – virtual medical consulting: A web‐based chronic kidney disease, hypertension and diabetes integrated care program
Bibliographic record
Abstract
AIMS: Chronic kidney disease patients overwhelm specialist services and can potentially be managed in the primary care (PC). Opportunistic screening of high risk (HR) patients and follow-up in PC is the most sustainable model of care. A 'virtual consultation' (VC) model instead of traditional face to face (F2F) consultations was used, aiming to assess efficacy and safety of the model. METHODS: +/- albuminuria >30 mg/mmol/L) were randomized to either VC or F2F. Patients were monitored in 6 monthly follow-up cycles by a Clinical Nurse Specialist. The specialist team provided virtual or clinical support and included a Nephrologist, Endocrinologist, Cardiologist and Renal 'Palliative' Supportive Care. RESULTS: Sixty one (87%) patients were virtually tracked or consulted with 14 (23%) being HR. At 12 months, there was no difference in outcomes between VC and F2F patients. All patients were successfully monitored. General practitioners reported a high level of satisfaction and supported the model, but found software integration challenging. Patients found the system attractive and felt well managed. Specialist consults occurred within a week, and if a second specialist opinion was required, it took another 2 weeks. CONCLUSIONS: The programme demonstrated safe, expedited and efficient follow up with a clinical and web based programme. Support from the general practitioners and patients was encouraging, despite logistical issues. Ongoing evaluation of VC services will continue and feasibility to larger networks and more chronic diseases remains the long term goal.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".