A Tribute to John Mendelsohn: A Pioneer in Targeted Cancer Therapy
Bibliographic record
Abstract
Abstract Cancer scientists and clinicians are mourning the death of one of the most accomplished members of their community: Dr. John Mendelsohn. He was a pioneer in targeted cancer therapy and was instrumental for the discovery and deployment of the first antagonist epidermal growth factor receptor (EGFR) therapeutic antibodies, broadening the concept of targeted EGFR therapy to encompass other receptor tyrosine kinases, such as HER2, and developing blocking antibody-combination therapy with chemotherapies or radiotherapy. Dr. Mendelsohn, who died on January 7, 2019, always led by the strength of his accomplishments and the humility of his character. Above all, he was a well-revered mentor and clinician, who extended compassion and the gift of his time to patients, colleagues, and mentees alike. In tribute to Dr. Mendelsohn, Cancer Research has invited his former mentees and colleagues who were associated with Dr. Mendelsohn for over three decades to reflect on the broad impact of his work. Here, we discuss Dr. Mendelsohn's illustrious career at three elite academic cancer institutions and hospitals in the United States, his acumen to build, grow, and uplift institutions, and train a generation of medical oncologists, physician scientists, and cancer biologists. His profound legacy on targeted therapy and cancer research and treatment continue to prolong and save the lives of cancer patients globally.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".