Vitamin D influences the prevalence of non-cutaneous carcinomas after kidney transplantation?
Bibliographic record
Abstract
Malignancy is a key factor that significantly reduces the graft and patient survival after kidney transplantation. Vitamin D (VD) is gaining attention for its pleiotropy, including neoplasia prevention. The aim of our study was to assess the possible association between de novo non-cutaneous carcinomas (non-cuCa) and the VD status in kidney transplant recipients (KTRs). All patients followed up in our transplant center were included in the study from May 2012 until May 2016. We compared KTRs with non-cuCa to those without carcinomas. The demographic characteristics, immunosuppression protocols and 25-hydroxyvitamin D levels were evaluated. Patients with unstable kidney function, renal transplant duration less than 5 years, other malignancies, cholecalciferol supplementation and outliers for VD were not included in the study. KTRs with virus-associated carcinomas were also excluded. The total 25-hydroxyvitamin D was measured by a validated liquid chromatography–tandem mass spectrometry (LC-MS/MS) method. Two hundred fifty-six patients met the selection criteria. Of these, 11 were detected with non-cuCa with different organ localisation. The VD deficient patients had higher non-cuCa prevalence compared to the rest of the cohort (16.7% vs. 3.4%, p = 0.034). The VD status was significantly lower in the patients with malignancy (39.27 ± 18.16 vs. 59.87 ± 22.82 nmol, p = 0.005). No other significant differences between the two groups were detected. Poorer VD status may be an independent risk factor for post-transplant non-cutaneous cancer. VD supplementation may be considered as an option to reduce non-cuCa prevalence after kidney transplantation.
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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 | 0.000 | 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) | 0.002 | 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".