Potential applications of telenephrology to enhance global kidney care
Bibliographic record
Abstract
Chronic kidney disease (CKD) is an important public health issue that increasingly affects more patients globally and is associated with adverse clinical consequences with huge economic impact. Effective management of patients with CKD requires delivery of kidney care in a primary care setting where possible and at a higher level with a nephrologist when necessary to improve outcomes. In many instances and for various reasons, it is not possible to follow this pathway of care delivery. With improving telecommunication technologies worldwide, it is hoped that increasing utilisation of electronic communication devices can be used to facilitate kidney care to improve the quality of care delivered to patients, especially those who live in remote regions. Kidney care and therefore outcomes for patients with CKD is often compromised due to lack of access to a nephrologist, either because of distance or shortage of nephrologists, high proportion of patients being unaware they have CKD, lack of population screening for early detection of CKD and risk factors and prevention programmes and poor patient adherence and absence of appropriate CKD management strategies. Telenephrology can play a significant role in addressing these factors and therefore can be leveraged to improve CKD outcomes globally, especially in low to middle-income countries. This paper provides an overview on the potential role of telenephrology in enhancing access to and quality of care delivered to patients with CKD to improve outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".