Factors affecting TST level in patients undergoing dialysis: a multicenter study
Bibliographic record
Abstract
INTRODUCTION: The risk of TB is increased in patients with chronic kidney disease (CKD) when compared with individuals with normal renal function. We aimed to determine tuberculin skin test (TST) response and the factors which might affect the response in patients with CKD undergoing dialysis in this study. METHODS: The purified protein derivative solution was administered to the patients and the diameter of induration was measured. Additionally, the age, gender and smoking status of the patients were interrogated. Comorbidities were recorded both by patients' self-reports and data from the hospital files. The number of Bacille Calmette-Guerin (BCG) scars was recorded by checking both shoulders. FINDINGS: The study was conducted with a total of 371 patients (194 men and 177 women). The mean age was 60.09 ± 15.88, TST was 6.99 ± 6.9, duration of dialysis was 4.44 ± 4.5 (3.8-0.1,24). A total of 229 patients have comorbodities (61.7%, the most frequent was hypertension). Logistic regression model was performed. Gender, vitamin D treatment and high parathormone (PTH) levels remained in the final stage of the analysis and vitamin D intake and PTH levels were detected to be statistically significant (P = 0.002, 0.007, respectively). DISCUSSION: This study is the first study which showed a correlation between TST negativity and increased PTH levels and receiving vitamin D treatment. Starting from this point, it was concluded that PTH may suppress the immune system and especially cellular immunity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".