Developing a pan-cancer research autopsy programme
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: Rapid procurement of a wide variety of metastatic and primary cancers and normal tissues after death through rapid autopsy opens largely unexplored avenues in cancer research. We describe a high-volume rapid research autopsy programme at a large academic medical centre. METHODS: Advanced-stage cancer patients, most commonly inpatients in palliative care facilities, were approached to participate in a cancer research autopsy programme with the goal of acquiring multidimensionally annotated tissue for cancer research. On death of an enrolled patient, a predetermined notification plan was enacted, with the medical oncologist/clinical research coordinator informing a team of pathologists, researchers and allied staff. Quality assurance metrics were measured. Thereafter, tissues were annotated in a tissue bioinformatics database and linked to electronic patient records. All banked tissues were reviewed for tumour integrity, including DNA and RNA quality. RESULTS: Over 100 rapid research autopsies from diverse cancer sites were performed, and specimens were procured and annotated with detailed clinical information, including treatment and response. Tissues were successfully enabling studies of tumour immunology, xenografts, genomics and proteomics. CONCLUSIONS: Large-scale rapid procurement and biobanking of cancer tissues from a rapid autopsy programme is feasible. Multidisciplinary integration between health and administrative staff from medical oncology, palliative care, pathology and biospecimen sciences is critical for the success of this challenging endeavour.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it