The Role of Biobanks in Oncology Research – A Portuguese Perspective
Bibliographic record
Abstract
Approximately, 14.1 million new cancer cases and 8.2 million deaths occurred in 2012 worldwide. Cancer is a group of diseases characterized by uncontrolled growth, spread of abnormal cells and is a leading cause of death in both more and less economically developed countries. The growth in our knowledge of cancer biology has led to remarkable progress in cancer prevention, early detection and treatment. Recent years have seen a remarkable progress in the basic, translational and clinical research in cancers. During carcinogenesis several genetic and epigenetic alterations occur and a molecular approach based on the detection of these molecular alterations can permit the definition of molecular signatures specific for different tumors, allowing the definition of molecular biomarkers. These markers can be found in a variety of fluids, tissues and cell lines, and can be used for screening the general population, for differential diagnosis in symptomatic patients and for clinical staging of cancer. Furthermore, the biomarkers can be used to evaluate response to treatment, to assess disease recurrence through monitoring and as prognostic indicators of disease progression. The advances in the molecular knowledge and technology from research is based on the study of different types of biological samples, being necessary the collection, processing, preservation, stored and maintenance of these samples required an ordered and structured plan with the consequent creation of a biobank. The promotion and operation of biobanks provide to scientists the opportunity to access to quality samples and associated data, representing an essential milestone in shortening the time between research and clinical application, improving the effectiveness of 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 | 0.060 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".