The Role of Diagnostic Tissue in Research
Bibliographic record
Abstract
There are two broad classes (or categories) of excised human tissue: diagnostic tissue (DT) and research tissue (RT). Classification of excised human tissue does not define its ultimate use and ultimate use of excised human tissue does not define its classification. While both DT and RT can be used for research, DT has specific requirements with respect to how it must be handled if and when being accessed for research. We highlight distinguishing features of DT: (1) it is a clinical record, (2) it must be identifiable to a specific individual, (3) it is stewarded by pathology departments/clinical laboratories and (4) it has a mandatory retention period. We discuss how the further sub-classification of DT into archived DT (aDT) and excess DT (eDT) impacts the nature of its role in research. We examine the concept of DT as a clinical record and emphasize the impact of mandatory retention as it applies to how DT may be accessed for research purposes. We explain the role of post-retention eDT as a source of RT as well as procedures for access to in-retention aDT for research. Clarity of such issues will facilitate responsible access to DT for research.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".