A Reflection on the Work of Gianni Bonadonna from the Viewpoint of the Global Challenge of Adolescents and Young Adults with Cancer
Bibliographic record
Abstract
Adolescents and young adults (AYAs - ages 15 to 39) constitute approximately 40% of the world's population and contribute an estimated one million new cases of cancer annually, the great majority in low- and middle-income countries (LMICs). In high-income countries (HICs) cancer is the commonest cause of disease-related death in AYAs, though overall 5-year survival rates now exceed 80%. A very different circumstance likely holds in LMICs, but accurate assessments are not readily available.Breast cancer accounts for 40% of tumours in female AYAs and this age group includes the peak incidence of Hodgkin lymphoma. The late Professor Gianni Bonadonna contributed importantly to improved survival in patients with these two diseases. Accordingly, he would be justifiably proud of the advances in AYA oncology that are being made in Italy, especially the impact of his colleagues at the Istituto Nazionale dei Tumori (INT). The initiatives of the Associazione Italiana Ematologia Pediatrica and the Società Italiana Adolescenti con Malattie Onco-ematologiche are particularly noteworthy, with the accomplishment of productive collaboration between paediatric and adult cancer care providers serving as a model for other countries to emulate.Exporting these advances can be successful through the vehicle of "twinning": establishing sustainable cooperation between institutions in HICs and partners in LMICs. Colleagues in Monza and at INT have been leaders in such programmes for decades. Cancer in AYAs remains a global challenge to which Gianni Bonadonna surely would have risen with enthusiasm and leadership while securing measurable achievements.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.023 | 0.071 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".