157P PCM4EU academy: An educational program for precision oncology
Bibliographic record
Abstract
The application of precision oncology needs to educate medical professionals and citizens, including patients and their advocates, researchers, molecular biologists, decision/policymakers, and funding bodies. Increasing the knowledge about new therapies and diagnostics tools, innovative clinical trial design, and innovation in research helps bridge better patient care to clinical practice. This project aims to build a comprehensive portfolio of knowledge for various audiences as podcasts and webinars and hybrid courses, including virtual molecular tumor boards. We also explore the different online channels for communication to reach the target population effectively. Between JAN-JUN 2023, 25 podcasts were recorded (12 in English, 3 in French, 10 in Polish). There are podcasts for medical professionals, and for citizens and patients (either in english or in local languages with subtitles available). Among others are podcasts on molecular screening techniques, the organization of precision medicine in EU, methodologies of personalized oncology clinical trials with information about ongoing DRUP-like trials, cross border access to therapies, pragmatic reimbursement and artificial intelligence in precision oncology. Two interactive training sessions, either in person or hybrid way were realized. Paper reports and recordings are available on the website. Educational materials are available from the project website, as video format on Youtube). Building an educational pathway by providing high quality educational materials is required to understand the complex and rapidly evolving field of precision oncology. The topics should be popularised also in national languages to reach high range of citizens.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.096 | 0.032 |
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".