Determinants of survival in patients receiving dialysis in <scp>L</scp>ibya
Bibliographic record
Abstract
Maintenance dialysis is associated with reduced survival when compared with the general population. In Libya, information about outcomes on dialysis is scarce. This study, therefore, aimed to provide the first comprehensive analysis of survival in Libyan dialysis patients. This prospective multicenter study included all patients in Libya who had been receiving dialysis for >90 days in June 2009. Sociodemographic and clinical data were collected upon enrollment and survival status after 1 year was determined. Two thousand two hundred seventy-three patients in 38 dialysis centers were followed up for 1 year. The majority were receiving hemodialysis (98.8%). Sixty-seven patients were censored due to renal transplantation, and 46 patients were lost to follow-up. Thus, 2159 patients were followed up for 1 year. Four hundred fifty-eight deaths occurred, (crude annual mortality rate of 21.2%). Of these, 31% were due to ischemic heart disease, 16% cerebrovascular accidents, and 16% due to infection. Annual mortality rate was 0% to 70% in different dialysis centers. Best survival was in age group 25 to 34 years. Binary logistic regression analysis identified age at onset of dialysis, physical dependency, diabetes, and predialysis urea as independent determinants of increased mortality. Patients receiving dialysis in Libya have a crude 1-year mortality rate similar to most developed countries, but the mean age of the dialysis population is much lower, and this outcome is thus relatively poor. As in most countries, cardiovascular disease and infection were the most common causes of death. Variation in mortality rates between different centers suggests that survival could be improved by promoting standardization of best practice.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".