Concept of a medical and public health cancer genomics platform
Bibliographic record
Abstract
Abstract Background This study aims to produce the concept of an open-access medical and public health cancer genomics platform aimed at aggregating and sharing documentation of ongoing initiatives and data resulting from the CAN.HEAL project, available for EU researchers, healthcare professionals, policy makers, citizens and patients. Methods The context (reference sector, stakeholders and research evidences), feasibility (privacy management and data processing), and platform’s end users were analysed; the CAN.HEAL project deliverables were listed and clustered; aspects related to the definition of the overall platform architecture and modules integration were studied. Results The project deliverables were catalogued into 8 categories which could be the platform’s cloud interface main labels: results from events, policy papers, newsletters, mapping results and reports, datasets, recommendations and guidelines, use cases, courses and training activities. To define the overall system architecture, the following key activities should be implemented: content management system choice, hosting configuration and setup of the development domain, content management system installation, development of HTML web pages, data entry, data visualisation and graphic layout, and test and debug. To the modules integration purposes the following items should be addressed: creation of a server analytics account; social network integration; activation of additional system for sending newsletters. Conclusions Cloud-based data platform allows to securely manage, integrate, analyse and share large datasets. Establishing an infrastructure to help researchers access, store and analyse large amounts of biological data is of paramount importance both to enable advances in research and support health policy making processes. The CAN.HEAL platform could help aligning clinical and population-based interventions for integrating the genome of Europe biobanking initiative into public health genomics for cancer. Key messages • Connecting healthcare data can support the challenge of accessing the relevant information needed for the policy and decision making processes to effectively promote population health and wellbeing. • The set-up of a medical and public health cancer genomics platform promotes the effective translation of genome-based knowledge and technologies into public policy and health services.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".