Abstract 926: Adaptive universal platform for real-world observational studies (AUPROS): An emerging model for clinical, epidemiologic, and precision oncology research
Bibliographic record
Abstract
Abstract Background: Adaptive Universal Platform for Real-world Observational Studies (AUPROS) is an emerging study design and platform for the generation of real-world evidence (RWE) particularly in malignancies with rare molecular aberrations. Here, we evaluate the efficiency of AUPROS as an innovative model for real-world observational studies in oncology. Methods: We conducted a mixed-methods study to evaluate three AUPROS studies at Princess Margaret Cancer Centre either locally or as a part of national and international collaborations: 1) METAL (Molecular Epidemiology of ThorAcic Lesions; n=11,378), 2) THANKS (Translational Head And NecK Study; n=3,506), and 3) CARMA (Canadian Cancers with Rare Molecular Alterations; NCT04151342; n=3,879). Specifically, we reviewed data collected, study elements, protocol language, coordination, institutional review boards (IRBs), and contracts. We also performed stakeholder-directed survey and discussions, analysis of funding, research output, and collaborations, as well as a Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis. Results: AUPROS is an innovative study design that borrows design elements from master protocol trials, adaptive trials, and master observational trials as well as retrospective and ambispective designs, enabling comprehensive data collection. The universality of AUPROS allowed for multi-purpose analyses of various real-world data (RWD) including epidemiological, clinical, patient-reported outcomes, biospecimens, and imaging data. The adaptive nature created opportunities for multi-source funding (e.g., academic, pharmaceutical, philanthropic, among others), as well as national/international collaborations and involvement in consortia (e.g., ILCCO, TCGA, BEACON, and INHANCE). Additionally, AUPROS has allowed the development of an expanding national RWE platform that involves collaborations among medical and radiation oncologists and surgeons across different tumor sites. As assessed by research output analyses, AUPROS provided a platform for enhanced research productivity in different areas and utilizing various data sources (METAL=130; THANKS=31; CARMA=5 publications). Our survey and SWOT analysis identified several cost and operational benefits of AUPROS over conventional observational studies, as well as several challenges pertinent to ethics approvals, sustainability, complex coordination, and data quality. Conclusions: AUPROS is an innovative model for real-world observational studies with significant logistical and methodological benefits over conventional RWD study designs. Our findings may help broaden the use of AUPROS to address emerging needs in the scope of precision oncology and clinico-epidemiological research. Citation Format: Samir H. Barghout, Stavroula Raptis, Luna Jia Zhan, Faisal Al-Agha, M Catherine Brown, Devalben Patel, Geoffrey Liu. Adaptive universal platform for real-world observational studies (AUPROS): An emerging model for clinical, epidemiologic, and precision oncology research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 926.
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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 | 0.614 | 0.674 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".