Use of Neoral C2 monitoring: a European consensus
Bibliographic record
Abstract
Large-scale clinical trials using C(2) monitoring of cyclosporine (CsA) microemulsion (Neoral) in renal transplant recipients have demonstrated low acute rejection rates and good tolerability with a low adverse event profile in a variety of settings: with or without routine induction therapy; in combination with mycophenolate mofetil; with standard-exposure or low-exposure Neoral; and in patients with immediate or delayed graft function. In liver transplantation, C(2) monitoring significantly reduces the severity and incidence of acute rejection compared with C(0) monitoring, without adverse consequences in terms of renal function or tolerability. Different C(2) targets are appropriate depending on adjunctive immune suppression, level of immunologic risk, CsA tolerability, risk of renal toxicity and time since transplantation. CsA absorption may increase substantially in most patients during the first 1-2 weeks post-transplant, and this should be taken into account to avoid overshooting C(2) target range. A patient with a low C(2) value may be either a low or a delayed absorber of CsA, or be a normal absorber who is receiving too low a dose of Neoral. C(2) monitoring alone is insufficient to differentiate between these types of patients, and measurement of additional timepoints is recommended. Adopting C(2) monitoring in maintenance transplant patients identifies those who are overexposed to CsA. In summary, randomized, prospective, multicenter studies and single-center trials have evaluated Neoral C(2) monitoring within a range of regimens in different organ types, providing a robust evidence base for the benefits of this sensitive monitoring technique.
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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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".