Bibliographic record
Abstract
Kidney disease, encompassing both acute and chronic forms, has emerged as a significant contributor to mortality in the 21st century (Kovesdy, 2022). With over 800 million individuals affected worldwide, the prevalence of chronic kidney disease (CKD) has reached alarming levels, impacting more than 1 in 7 U.S. adults alone (Jager, 2019). Despite its widespread prevalence, a considerable number of individuals remain unaware of their condition, leading to disparities in care and outcomes (Centers for Disease Control and Prevention, 2021). Moreover, racial disparities persist, with Black individuals disproportionately affected by end-stage kidney disease (ESKD) in the United States (United States Renal Data System, 2022). The economic burden of kidney disease is substantial, with Medicare spending for beneficiaries with CKD surpassing $75 billion in 2020 (United States Renal Data System, 2022). This financial strain is particularly concerning in lower-income countries where there is limited or no health coverage for kidney disease treatment. In Africa, notably in Ghana, kidney disease occurrence rates are notably high, often linked with conditions such as glomerulonephritis, diabetes mellitus, and hypertension (Amoako et al., 2014). Despite the pressing need for kidney care, marginalized communities encounter obstacles in accessing care, underscoring the importance of exploring innovative solutions such as telemedicine (Lambooy et al., 2021). The literature review underscores the potential of telemedicine in reducing healthcare costs, minimizing infections, and expediting pretransplant evaluations. Recent research indicates that telehealth significantly improves the efficiency of initial waitlist evaluations for kidney transplantation, leading to better prognoses (Ammary FA et al. 2021). However, challenges in establishing suitable locations for telehealth video conferencing, particularly in the USA due to stringent guidelines, remain (Conception et al. 2020). Notably, there is currently no telemedicine guideline in Ghana. Despite associated costs with telehealth tools and personnel training, they are considered moderate and reliable, with specialized training deemed crucial for effective utilization (Forbes RC et al., 2018). Integration of telehealth in kidney transplant care has demonstrated enhanced efficiency and cost-effectiveness, particularly in initial waitlist evaluations(Forbes RC et al., 2018). Telemedicine holds promise in enhancing access to kidney care in rural areas, with outcomes comparable to standard care. Therefore, conducting a study in Ghana to investigate the feasibility of telemedicine in kidney care is essential for improving access and reducing healthcare costs. Additionally, this research may shed light on the need for increased efforts to develop telemedicine guidelines and provide specialized training to healthcare professionals, particularly in resource-limited settings like Ghana. Vanessa E Silva.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".