Bibliographic record
Abstract
PURPOSE OF REVIEW: Inadequate cyclosporine exposure is a key risk factor for acute rejection, and may contribute to the development of chronic rejection and graft failure. Pre-dose monitoring does not accurately measure drug exposure because of extensive inter- and intra-patient variability in cyclosporine absorption and metabolism. Limited sampling, using individual timed specimens, offers a new, simple and accurate alternative for clinical monitoring of cyclosporine. RECENT FINDINGS: The area under the first 4 h of the concentration-time curve (AUC ) and the single-point concentration at 2 h post-dose (C2) are key measures of cyclosporine exposure. De novo studies show that achieving an AUC value of more than 4400 microg.h/l or a C2 level of 1500-2000 microg/l during the first 5 days post-transplant minimizes the risk of rejection and improves graft function. Maintenance studies suggest that reducing the C2 level to approximately 800 microg/l after 3-6 months may improve the serum creatinine level, blood pressure, general well-being and reduce adverse effects. SUMMARY: Single-point C2 monitoring can be implemented quickly and simply with appropriate site and patient training. The timing of phlebotomy is more critical, but immunoassay bias is lower with 2 h post-dose than with trough level measures. Single-point C2 monitoring may be effective in liver and heart replacement, but initial target levels for liver transplantation are lower because cyclosporine is transported directly to the liver via the portal system. C2 monitoring is now being widely adopted as an accurate and practical measure of drug exposure, and can be combined with pharmacodynamic methods to optimize immunosuppression.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".