Seasonal Variation of Serum Lipid Levels in Stable Renal Transplant Recipients
Bibliographic record
Abstract
BACKGROUND/AIMS: Seasonal variation in lipid levels is well described in the general population, but has not been examined in renal transplant recipients (RTR). We sought to determine whether seasonal differences exist in RTR, a group at high risk for hyperlipidemia. METHODS: We reviewed our population of 920 adults, identifying primary allograft recipients with survival > or =1 year, stable function, and > or =1 pair of post-6 months 'winter' (December 21 to March 20) plus 'summer' (June 21 to September 22) fasting lipid measurements within the same year. Correlations between factors affecting lipids and lipid level change were followed by multiple linear regression analysis. RESULTS: 243 patients contributed 344 pairs. When most recent seasonal pair (n = 243) and all pairs (n = 344) were separately analyzed, no seasonal total cholesterol difference (winter vs. summer) was seen (5.08 vs. 5.05 mmol/l, p = 0.80; 5.11 vs. 5.09 mmol/l, p = 0.81 respectively). Opposing variation was seen between hyperlipidemic and nonhyperlipidemic patients (0.08 vs. -0.18 mmol/l for winter minus summer, p = 0.02). In multivariate analysis, seasonal cholesterol variation was predicted by level (p < 0.0001) and hemoglobin change (p = 0.01), while triglyceride variation was predicted only by level (p = 0.01). CONCLUSION: RTR do not exhibit seasonal variation in lipids, unlike the general population. Factors unique to RTR such as immunosuppressive therapies may act to suppress any seasonal effects.
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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 | 0.002 |
| 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 | 0.000 | 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".