Evaluating the accuracy of functional biomarkers for detecting histological changes in chronic allograft nephropathy
Bibliographic record
Abstract
The most common cause of late kidney transplant failure is chronic allograft nephropathy (CAN). Much research has focused on identifying biomarkers (or correlates) that would predict subsequent CAN and allow timely intervention. Functional biomarkers such as serum creatinine and estimated glomerular filtration rate (eGFR) have been widely adopted, even though they have not been rigorously evaluated as surrogate markers. This study evaluated serum creatinine and eGFR for predicting the early histopathological changes seen in transplant protocol biopsies (TPB). We prospectively followed 289 kidney transplant patients in the Southern Alberta Transplant Program who had TPB at 6-12 months post-transplant. Tissue samples (n = 280) were independently examined by renal pathologists. The ability of serum creatinine or eGFR to predict the threshold level for abnormal histopathology was evaluated by calculating the area under the receiver operator characteristic curve. Serum creatinine and eGFR had poor predictive value (most confidence intervals included 0.5, indicating no predictive ability) for ten individual histological measurements (Banff 97 scores), and the Chronic Allograft Damage Index. We conclude that serum creatinine and eGFR have a limited clinical role in predicting the early histopathological changes that precede CAN and should not be used for this purpose.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".