Interpreting Gene-Expression Profiles in Transplantation: A Critical Appraisal
Bibliographic record
Abstract
Oligonucleotide microarray technology has created a small revolution in the transplant community because it has helped to decipher previously unknown molecular processes involved in allograft pathology, redefined molecular patterns of diseases that are indistinguishable at the pathological level and made possible the definition of new prognostic factors for long-term graft outcomes. However, given the tremendous complexity of the biological processes that are involved in the pathology of a transplanted organ, the interpretation of transcriptomic data can be speculative and oversimplified. Here, we discuss critical considerations regarding the nature of the object studied by cDNA microarray technology, the means by which it is observed, the interpretation of the observations, and whether the observations make sense in the context of transplant-related scientific questions. Given these limitations, we believe that global approaches based on more functional biological intermediates are necessary for a better understanding of the molecular processes that regulate the physiopathology of the transplanted organ.
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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 | 0.062 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".