Challenges of providing maintenance hemodialysis in a resource poor country: Experience from a single teaching hospital in <scp>L</scp>agos, <scp>S</scp>outhwest <scp>N</scp>igeria
Bibliographic record
Abstract
Providing maintenance hemodialysis is associated with high costs and poor outcomes. In Nigeria, more than 90% of the population lives below the poverty line, and patients with end-stage renal disease (ESRD) pay out-of-pocket for maintenance hemodialysis. To highlight the challenges of providing maintenance hemodialysis for patients with ESRD in Nigeria, we reviewed records of all patients who joined the maintenance hemodialysis program of our dialysis unit over a 21-month period. Information regarding frequency of hemodialysis, types of vascular access for dialysis, mode of anemia treatment and frequency of blood transfusion received were retrieved. One hundred and twenty patients joined the maintenance hemodialysis program of our unit during the period under review. Seventy-two (60%) were males and the mean age of the study population was 47 + 14 years. The mean hemoglobin concentration at commencement of dialysis was 7.3 g/dL + 1.6 g/dL. The initial vascular access was femoral vein cannulation in all the patients. A total of 73.5% of the patients required blood transfusion at some point with 33% receiving five or more pints of blood. Only 3.3% of the patients had thrice weekly dialysis, 21.7% dialyzed twice weekly, 23.3% once weekly, 16.7% once in two weeks, 2.5% once in three weeks and 11.7% once monthly. At the time of review, 8.3% of the patients had died while 38.3% were lost to follow-up. Majority of patients with ESRD on maintenance hemodialysis in our unit were poorly prepared for dialysis, were under-dialyzed, and were frequently transfused with blood with resultant poor outcomes.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".