Comparing Molecular Assessment of Implantation Biopsies With Histologic and Demographic Risk Assessment
Bibliographic record
Abstract
We hypothesized that measurement of previously defined acute kidney injury-induced transcripts at the time of implantation would add a new dimension to existing methods based on donor factors, histology and recipient factors. We analyzed microarray results from implantation biopsies taken after reperfusion from 70 kidneys from 53 deceased donors. We used two definitions of early dysfunction: serum creatinine > 265 umol/L at day 7 posttransplant; and dialysis in the first week. The strongest correlate with early dysfunction was the mean expression of 30 injury transcripts. Older donor and recipient age were associated with early dysfunction, but histologic lesions were not. Prediction was best when the injury transcript expression was combined with donor or recipient age, particularly in standard criteria donors. In contrast, although extended criteria donor kidneys had a high risk of early dysfunction, no variables tested, including injury transcripts, predicted risk significantly, probably because these kidneys were allocated preferentially to old, high risk recipients. The injury transcripts did not predict late function, which was mainly associated with donor age. Thus, measurement of injury-induced transcripts at the time of implantation improves the prediction of early kidney dysfunction, but risk prediction may fail when old kidneys are transplanted into old recipients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".