Is C2 Monitoring or Another Limited Sampling Strategy Superior to C0 Monitoring in Improving Clinical Outcomes in Adult Liver Transplant Recipients?
Bibliographic record
Abstract
Cyclosporine (CsA) has had a major impact on the process and success of solid organ transplantation. Early in the use of CsA, therapeutic monitoring using the predose (trough, or C0) concentration became the standard of care. However, there are complications with the use of C0 monitoring that have only partly been mitigated with the advent of the micro-emulsion formulation (CsA-ME). More recently, limited sampling strategies (LSSs) for measuring the area under the CsA concentration-time curve (AUC) have been investigated to improve the monitoring of CsA post-transplantation. Many centres now routinely monitor CsA therapy using the concentration at 2 hours postdose (C2). In this paper the strength of the evidence for C2 (or other LSSs) relative to C0 monitoring of CsA-ME for improving clinically important outcomes in liver transplant patients is critically examined. Additionally, gaps in the literature are identified and recommendations are made for clinical research that could be done to provide more definitive evidence for the use of C2 or other LSSs in monitoring liver transplant patients.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".