Frozen in Translation: Biobanks as a Tool for Cancer Research
Bibliographic record
Abstract
In the context of translational cancer research, biobanks are key infrastructures that provide high quality biological samples, coupled with relevant clinical and pathological information. This role can only be successfully accomplished through the implementation of standardized procedures that ensure proper collection, handling, processing, storage and recording of tissue samples, following strict legal and ethical regulations. Biobank networking is fundamental for dissemination of good practices and to help in the establishment of new infrastructures that improve the assessment of heterogeneity among tumor types and across patient cohorts. Growing demands for large number of homogenously preserved tumor tissue samples can only be met through a more intense cooperation among biobanks, facilitated by networks that foster cooperation at international level. The potential of biobanks as fundamental tools for translational cancer research can only be achieved through a concerted effort from biobankers, researchers, legislators and tissue donors that may allow for improved sample exchange.
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.
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 | 0.143 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".