Abstract B060: DIGIONE: From Fragmentation to Federation: Enabling Scalable Oncology RWE (Real World Evidence) through an international hospital based Cancer OMOP (Observational Medical Outcomes Partnership) Network
Notice bibliographique
Résumé
Abstract Real-world evidence in oncology today is largely “one question, one dataset” — and as a highly bespoke, slow, and fragmented. Pharma and academic teams have to stitch together disparate sources (such as registries or research cohorts) for each new regulatory, HTA (Health Technology Assessment) submission or AI development, with little scope for automation or reuse. This is because clinical registries and cohorts today remain siloed and driven by manual retype, and so lack alignment on common data models and paths to automation. This is strongly hampering the wide spread application of AI-driven RWE models at the international level. DIGICORE (Digital Institute for Cancer Outcome Research) is changing this by operationalising a pan-European hospital network with high quality real-world cancer data mapped to the Cancer OMOP model. A core enabler of this work is the development of MEDOC – a Minimal European Description of Caner agreed by international consensus as essential to the delivery of good cancer care. Over 20 hospitals have already mapped their EHR (Electronic Health Record) data to deliver MEDOC—covering demographics, clinical phenotype, biomarker, treatment activity, and pragmatic outcomes —enabling multi-country cohort assembly using shared protocols, with high data completeness from local NLP. Advanced analytics take OMOP tools from meta-analysis to patient level equivalence. We call this technology network DigiONE – the Digital Oncology Network for Europe. Disease Natural History and care quality assessment studies in Lung, Breast, and Colorectal cancer are already underway on large cohorts, with hospital readiness assessed through real study execution. To date, three projects have been completed, with additional 10 projects underway. Project 1: Analysis of the number of new primary cancers diagnosed and 12-month survival changes during COVID-19 lockdowns (124.682 patients). Primary Objectives: To Investigate the impact of COVID19 lockdowns on new cancer diagnoses. To estimate 12-month survivals. Project 2: A disease natural history and outcomes study with care quality assessment in metastatic non-small cell lung cancer (1294 patients). Primary Objectives: To investigate the survival of patients according to the location of the metastases. To describe treatment patterns by line of therapy prescribed to patients: All the cohort, Subgroups by locations of the metastases (oligometastatic cohort). To benchmark care quality between centers based on ESMO (European Society of Medical Oncology) recommendation. Project 3: A disease natural history and outcomes study with care quality assessment in HR+/HER2- metastatic breast cancer (5k-10k patients). Primary Objectives: To Describe the demographic, clinical, molecular phenotypes, and next-generation sequencing (NGS) results for patients with HR+/HER2− metastatic breast cancer (mBC). Citation Format: Alberto Traverso, Piers , Xose' Fernandez, Golbahar Pahlavan, Giovanni Tonon. From Fragmentation to Federation: Enabling Scalable Oncology RWE (Real World Evidence) through an international hospital based Cancer OMOP (Observational Medical Outcomes Partnership) Network [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B060.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,066 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,003 | 0,017 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,012 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».