Free dialysis in Nepal: Logistical challenges explored
Bibliographic record
Abstract
Nepal's Ministry of Health began offering free lifetime hemodialysis (HD) in 2016. There has been a large growth in renal replacement therapy (RRT) services offered in Nepal since 2010, when the last known data on the subject was published. In 2016, 42 HD centers existed (223% increase since 2010) serving 1975 end stage renal disease patients (303% increase since 2010); 36 nephrologists were registered (200% increase since 2010), 12 were trained in transplantation, and 790 transplants had been performed to date. We estimate the incidence of end stage renal disease to be 2900 patients (100 per million population). With an annual cost of approximately US$2300 per dialysis patient, offering free dialysis could potentially cost the government US$6.7 million per year, suggesting that 2.1% of the annual health budget would be allocated to 0.01% of the population. The geographic zone surrounding the capital city, Kathmandu, contains 50% of HD centers, but only 14.5% of Nepal's population. Forty-eight percent of the population lives within zones without HD service, therefore infrastructure challenges exist in providing equitable access to RRT. The aim of this article is to summarize the current statistics of RRT in Nepal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".