Subclinical rejection--a potential surrogate marker for chronic rejection--may be diagnosed by protocol biopsy or urine spectroscopy.
Bibliographic record
Abstract
Our studies of protocol biopsy studies have shown that normal allograft histology can not be assumed by crude tests of renal function such as the serum creatinine concentration, and that there is a high prevalence of subclinical rejection in the first 6 months post-transplant (7, 13-17). The apparent ability of urine MR and IR spectra to reliably identify patients with normal allograft histology, if confirmed in a larger database, will preclude the need for a protocol biopsy in approximately 20-50% of patients. Conversely, finding urine MR or IR spectra characteristic of subclinical rejection would provide the opportunity for early treatment. The clear separation between patients with normal histology from those with subclinical rejection can be attributed to the use of the whole urine spectrum to develop the classifiers. Additional advantages of using MR or IR spectra of urine as a diagnostic tool compared to the biopsy include simplicity (i.e. no processing is required), low cost, rapid turnaround (i.e. < 15 minutes/sample), and, particularly, low risk, thus allowing for repetitive sampling. The ability to non-invasively diagnose acute inflammation in the kidney would be of great assistance in the post-transplant monitoring of renal transplant patients. Indeed, by following subclinical inflammation as detected in the MR/IR spectra it will be possible to tailor the intensity of the immunosuppression to the inflammatory status of the graft, thus minimising the risks of both insufficient and excessive immunosuppression. Furthermore, by following subclinical inflammation, as detected in the MR/IR spectra, it will be possible to test the hypothesis that subclinical rejection (i.e. persistence of its MR/IR spectral classifier) is a surrogate marker for the development of chronic rejection.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".