Tuberculosis in patients on hemodialysis in an endemic region
Bibliographic record
Abstract
Clinical presentation of tuberculosis is different in hemodialysis patients than in the general population. This study aimed to analyze hemodialysis patients with tuberculosis in Istanbul. Patients who were on a chronic hemodialysis program in Istanbul for more than 3 months and diagnosed to have tuberculosis at least 3 months after the start of hemodialysis were included. To discard the effect of immigration from other cities, we included only patients who had started their dialysis program in Istanbul. Their demographic and clinical data were analyzed using Statistical Package for Social Sciences for Windows ver. 13.0. Of the 925 patients screened from 7 different centers, 31 (3.35%) were found to have tuberculosis. The mean age was 52.3±13.5 years. The male/female ratio was 18/13. The mean duration of dialysis therapy and the duration of dialysis till the diagnosis of tuberculosis were 62.6±54.3 and 21.7±25.7 months, respectively. Extrapulmonary tuberculosis constituted 48.39%. Treatment ended with a cure in 18 (58.05%); was still ongoing in 12 (38.70%) patients; and 1 (3.25%) died of pulmonary tuberculosis. The lower incidence of tuberculosis compared with previous reports may be related to the differences in the diagnostic criteria and the decrease in the rate of tuberculosis during recent years. The demographic and clinical parameters of the patients were quite similar to the average dialysis population in Turkey. Hence, we cannot address a subpopulation with additional risk. It is important to prevent tuberculosis in hemodialysis patients due to difficulties in the diagnosis and treatment. Thus we recommend routine screening of hemodialysis patients and effective isolation and treatment of infected patients.
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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 | 0.000 |
| 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".