Post‐mortem tissue donation programs as platforms to accelerate cancer research
Bibliographic record
Abstract
Given recent advances in the treatment of cancer, patients are surviving longer but frequently develop treatment-resistant and inoperable metastases. Biomedical research has advanced to the stage where in-depth study of these lesions is feasible, with the goal of further refining our understanding of metastatic dissemination, therapeutic resistance and inoperable tumors. However, there is a lack of tissue specimens derived from multiple metastatic sites within the same patient that would permit the study of these processes. Furthermore, patients with rapidly progressing or metastatic disease are rarely candidates for surgery, making those most in need of innovation and discovery extremely difficult to study. For this reason, post-mortem tissue donation programs are an approach that is quickly gaining traction in the cancer research community. Herein, we discuss what post-mortem tissue donation entails, attitudes towards these procedures, and highlight important studies already utilizing these resources. In addition, we propose future directions for use of this tissue that can directly improve clinical management of advanced cancer patients.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".