Mass Spectrometry-based Cancer Biomarker Discovery: Current Pitfalls and Future Perspectives. An Interview with Dr. Eleftherios P. Diamandis
Bibliographic record
Abstract
Dr. Eleftherios P. Diamandis is a Professor at the University of Toronto and a researcher in the Samuel Lunenfeld Research Institute of Mount Sinai Hospital. His lab integrates enzymology, cell biology, proteomics and translational research in the pursuit of discovering biomarkers that could aid in early detection and monitoring of cancer and other diseases. With over 25 years of experience in analytical chemistry and clinical biochemistry, Dr. Diamandis is recognized as a leading researcher in the cancer biomarker field. In addition to his Biochemist-in-Chief position at University Health Network, Dr. Diamandis is the Division Head of Clinical Biochemistry at Mount Sinai Hospital and a Professor in the Department of Laboratory Medicine and Pathobiology at University of Toronto.
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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 | 0.033 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".