Identifying endpoints to predict the influence of immunosuppression on long‐term kidney graft survival
Bibliographic record
Abstract
Identifying a short-term endpoint for use in clinical trials that accurately reflects the influence of specific immunosuppressive regimens on long-term kidney graft survival is challenging. The number, timing, type (T-cell-mediated or antibody mediated), and severity of biopsy-proven acute rejection (BPAR) episodes in terms of histological changes and functional impact are highly influential for graft prognosis, and a crude measure of overall acute rejection incidence alone is unlikely to be a robust predictor of graft outcome. A series of studies has shown remarkably consistent results in terms of the cutoff point for one-yr renal function which predicts poor long-term graft survival, indicating that a threshold of 50 mL/min/1.73 m(2) is likely to be appropriate. Estimated glomerular filtration rate at one yr post-transplant discriminates effectively among immunosuppressive regimens with regard to graft survival, primarily calcineurin inhibitor reduction strategies. Several other factors that can affect graft survival, such as pathological changes in the graft, may be partly influenced by the immunosuppressive regimen, but the contribution of drug therapy is difficult to define. A combined approach in which both treated BPAR and renal function at one yr are used to assess novel immunosuppressive regimens appears to be promising as the emphasis shifts toward sustaining kidney allograft survival over the long term.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".