Bibliographic record
Abstract
There are few organized data on the practice of dialysis in developing countries, mostly because of a lack of renal registries. The economic, human, and technical resources required for long-term dialysis make it a major economical and political challenge. Most countries do not have not well-formed policies for treatment of end-stage renal disease. The dialysis facilities are grossly inadequate, and there are no reimbursement schemes to fund long-term dialysis. Hemodialysis units are mostly in the private sector and consist of small numbers of refurbished machines. Water treatment is frequently suboptimal, and this problem has led to a number of complications. Hepatitis B and C infections are widespread in dialysis units. Continuous ambulatory peritoneal dialysis (CAPD) seems to be the ideal dialysis option for patients living in remote areas, but high costs preclude its widespread usage. The Mexican experience suggests that even after it becomes affordable, CAPD needs to be used judiciously. Inadequate dialysis, infections, and malnutrition account for the high mortality among the dialysis population in developing countries. Acute peritoneal dialysis using rigid stylet-based catheters is the main form of dialysis in remote areas. Pediatric dialysis units are almost nonexistent. A significant lack of resources exists in developing countries, making the provision of highly technical and expensive care like dialysis a challenge.
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 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".