Bibliographic record
Abstract
Renal transplant success is closely tied to the ability to monitor transplant recipients and responsively change their medications. However, transplant monitoring still depends on relatively dated technologies - serum creatinine levels, urine output, and histopathology of biopsy samples. These techniques do not offer sufficient specificity, sensitivity, or accuracy for appropriate and timely interventions. As a result, more specific diagnostic techniques, based on proteomics, genomics and metabolomics are being sought. Metabolomics (the high-throughput measurement and analysis of metabolites) may make it possible to monitor transplants more effectively and specifically. Changes in the concentration profiles of a number of small molecule metabolites found in either blood or urine can be used to localize kidney damage, assess organs at risk of rejection, assess kidneys suffering from ischemiareperfusion injury or identify organs that have been damaged by immunosuppressive drugs. The application of metabolomics to kidney transplant monitoring is still in its early stages. Nevertheless, there are a number of easily measured metabolites in both urine and serum that can provide reliable indications of kidney function, kidney injury, and immunosuppressive drug toxicity. Metabolomics could serve as a good complement to existing proteomic and genomic technologies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".