A quality of life comparison in cyclosporine- and tacrolimus-treated renal transplant recipients across Canada
Bibliographic record
Abstract
Abstract Background: Many Canadian renal transplant recipients receive either cyclosporine or tacrolimus as a long-term immunosuppressive agent. We investigated the effect of these drugs on quality of life (QoL) in Canadian transplant recipients. Methods: We included adult single-organ recipients undergoing a transplant between July 1997 and March 2005, whose graft function was ≥18 months, recruited across 13 Canadian sites including 5 transplant centers (TCs) and 8 satellite centers (SCs). Patients were stratified 3:1 by cyclosporine vs. tacrolimus based on calcineurin inhibitor(s) (CNIs) received at 6 months posttransplant and matched 1:1 by TC vs. SC. Physical (PCS) and mental component summary (MCS) scores measured by the SF-12 scale for cyclosporine- and tacrolimus-treated recipients were compared. Patient opinions about their perceived CNI-related side effects captured by categorical questions or a numerical Likert scale (1-10) were compared by chi-square test or ANOVA, respectively. Results: There were 231 participants (124 cyclosporine, 43 tacrolimus and 64 with dual experience) who responded to both questionnaires. Their SF-12–measured PCS and MCS scores were similar (PCS 42.0, 43.0 and 41.4, p=0.705; MCS 50.3, 47.8 and 47.1, p=0.115; respectively). However, patients receiving tacrolimus more strongly preferred to continue on this CNI than those receiving cyclosporine (67.4% vs. 44.4%, p=0.009), while more patients on cyclosporine wished to stop taking it (23.4 vs. 2.3%, p=0.004). Patient preference for CNI did not differ by center type. Conclusion: QoL among Canadian renal transplant recipients receiving cyclosporine or tacrolimus is similar. Although Canadian recipients prefer tacrolimus, CNI type does not significantly affect their QoL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".