Resource settings have a major influence on the outcome of maintenance hemodialysis patients in South India
Bibliographic record
Abstract
Chronic kidney disease is reaching epidemic proportions and the number of patients on renal replacement therapy (RRT) is increasing worldwide and also in developing countries. To meet the challenge of providing RRT, a few charity organizations provide hemodialysis units for underprivileged patients, as the private hospitals are unaffordable for the majority. There is a paucity of information on the outcome of dialysis in these patients. Here, we describe the outcome of hemodialysis patients comparing the middle- and upper-class income group with the lower class income group. A retrospective analysis was carried out in 558 CKD patients initiated on maintenance hemodialysis in two different dialysis facilities. Group A (n=247) included those who belonged to the lowermost socioeconomic status and were undergoing dialysis in two nonprofit, charity (TANKER)-run dialysis units, and Group B (n=311) was undergoing dialysis in a nonprofit hospital setting where no subsidy was given. Those patients of a low socioeconomic status, especially those who are diabetics, have a higher death rate (Group A-38.1%, Group B-4.2%) and loss to follow-up (Group A-25.9%, Group B-0.3%) compared with those who are in the middle- and high-income group. Higher EPO use and hence higher hemoglobin levels (Group A-6.4+/-1.2, Group B-8.9+/-1.5 P<0.001) were observed in those who were in the middle and the higher income group. Lower serum phosphorus level was observed in the low-socioeconomic group (Group A-4.7+/-1.5, Group B-5.5+/-1.9, P<0.001). Patients belonging to the middle and higher socioeconomic group undergo more transplantations compared with the lower socioeconomic group (Group A-2.4%, Group B-65.6%).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".