ESMO / ASCO Recommendations for a Global Curriculum in Medical Oncology Edition 2016
Bibliographic record
Abstract
The European Society for Medical Oncology (ESMO) and the American Society of Clinical Oncology (ASCO) are publishing a new edition of the ESMO/ASCO Global Curriculum (GC) thanks to contribution of 64 ESMO-appointed and 32 ASCO-appointed authors. First published in 2004 and updated in 2010, the GC edition 2016 answers to the need for updated recommendations for the training of physicians in medical oncology by defining the standard to be fulfilled to qualify as medical oncologists. At times of internationalisation of healthcare and increased mobility of patients and physicians, the GC aims to provide state-of-the-art cancer care to all patients wherever they live. Recent progress in the field of cancer research has indeed resulted in diagnostic and therapeutic innovations such as targeted therapies as a standard therapeutic approach or personalised cancer medicine apart from the revival of immunotherapy, requiring specialised training for medical oncology trainees. Thus, several new chapters on technical contents such as molecular pathology, translational research or molecular imaging and on conceptual attitudes towards human principles like genetic counselling or survivorship have been integrated in the GC. The GC edition 2016 consists of 12 sections with 17 subsections, 44 chapters and 35 subchapters, respectively. Besides renewal in its contents, the GC underwent a principal formal change taking into consideration modern didactic principles. It is presented in a template-based format that subcategorises the detailed outcome requirements into learning objectives, awareness, knowledge and skills. Consecutive steps will be those of harmonising and implementing teaching and assessment strategies.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.064 |
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".