Addition of anti‐CD25 to thymoglobulin for induction therapy: delayed return of peripheral blood CD25‐positive population
Bibliographic record
Abstract
Sageshima J, Ciancio G, Gaynor JJ, Chen L, Guerra G, Kupin W, Roth D, Ruiz P, Burke GW. Addition of anti‐CD25 to thymoglobulin for induction therapy: delayed return of peripheral blood CD25‐positive population. Clin Transplant 2011: 25: E132–E135. © 2010 John Wiley & Sons A/S. Abstract: An anti‐CD25 monoclonal antibody was added to thymoglobulin for induction therapy in simultaneous pancreas/kidney (SPK) recipients. T‐cell subsets including CD3 and CD25 were assessed by flow cytometry analysis in the peripheral blood of SPK (n = 88), and for comparison kidney transplant (KT) recipients were assessed. KT recipients were treated with daclizumab (anti‐CD25) alone (five doses; 1 mg/kg) (n = 27) or thymoglobulin alone (4–7 doses; 1 mg/kg) (n = 23). SPK recipients received daclizumab (two doses; 1 mg/kg) in addition to thymoglobulin (five doses; 1 mg/kg). The return of peripheral blood CD25+ cells was delayed for 45 d post‐transplantation in the SPK recipients where anti‐CD25 was added to thymoglobulin, compared to those KT recipients with thymoglobulin alone. This strategy may result in reduced allogeneic (donor‐specific) T effector cells at the time of solid organ transplantation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".