Health-related quality-of-life outcomes of sirolimus-treated kidney transplant patients after elimination of cyclosporine A: results of a 2-year randomized clinical trial1
Bibliographic record
Abstract
BACKGROUND: This study compared 2-year health-related quality-of-life (HRQL) outcomes of sirolimus (SRL)-treated kidney transplant patients after elimination of cyclosporine A (CsA) to patients continuing on a combined CsA and SRL regimen. METHODS: A randomized, open-label, clinical trial was performed in Europe, Australia, and Canada. Four hundred thirty kidney transplant patients were randomly assigned to sirolimus plus steroids (ST) (n=215) or SRL and CsA+ST (n=215) therapy after 3 months of combined SRL+CsA+ST treatment. HRQL was measured using the Kidney Transplant Questionnaire (KTQ) and the SF-36 Health Survey at month 3 (time of randomization) and months 12 and 24 after transplantation. Repeated-measures analysis of covariance was used to evaluate treatment differences in HRQL scores over the 2-year period. RESULTS: HRQL scores were available for 361 (86%) eligible study patients. Statistically significant treatment-by-assessment time interactions, favoring SRL+ST, were found on KTQ Fatigue (P=0.0158) and Appearance scores (P=0.0007). No treatment differences were observed in KTQ Physical Symptom, Uncertainty-Fear, and Emotion scores. Statistically significant treatment-by-assessment time interactions were observed for SF-36 Vitality scores (P=0.0203) but not other SF-36 scores (P>0.05). For Vitality scores, the SRL+ST group remained stable (mean, 0.4-point change) from month 3 to month 24 compared with decreases in the SRL+CsA+ST group (mean, -6.5-point change). CONCLUSIONS: SRL-based therapy with early elimination of CsA results in fewer appearance-related problems, less fatigue, and better vitality compared with continuous treatment with SRL, CsA, and ST.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".