Study design of a global molecular disease characterization initiative (MDCI) in oncology clinical trials.
Bibliographic record
Abstract
e13598 Background: Current clinical trial selection for patients with independent screening for each trial, results in high screen failure and limited options for ineligible patients. MDCI’s concept for patient screening centers around broad molecular analysis and one screening protocol for multiple trials to increase patient inclusion and shorten recruitment time for oncology clinical trials.. Methods: MDCI was designed in collaboration with patients, physicians and study sites. Feedback from the Oncology Patient Council (OPC) was solicited beginning at study conception with input on study design, the informed consent form and the Gather Share Know participant portal. Patients provided specific detailed feedback and user acceptance throughout development to ensure a truly patient-focused approach. To track implementation of feedback, the MDCI team developed a document, which was shared with OPC, recording all feedback received and all actions taken by the study team. Feedback from study sites led to additional flexibility for visits (ie, combining study visits 1 and 2; allowing for telehealth visits for visit 3) and collection of data on medical history and prior therapies to streamline the screening process. Physician input included the acceptance of next generation sequencing (NGS) to determine the best therapy for each patient. Results: The MDCI protocol combines analysis of patient medical history, blood, and tumor assays, including HLA expression, protein analyses and NGS. A trial-matching approach, developed in collaboration with IQVIA, identifies potential clinical trials based on screening results. The Gather Share Know Hub, an optional patient-facing portal, allows patients to view the screening results identified as important for patients and information about ongoing clinical trial options. Patients also have access to a patient-friendly informational video, disease-specific education, credible resources and information on “what to expect” at study visits. Physicians receive clinical reports and molecular profiles from multiple screening tests (available through the Physician Portal), enabling them to make informed, data-driven decisions on the best clinical trial option for each patient. Conclusions: Utilizing a collaborative approach, MDCI was developed as a novel tumor-profiling protocol. MDCI is designed to rapidly prescreen patients for multiple studies at once by evaluating each patient’s tumor and blood genetics as well as their medical and cancer history using a prescreening algorithm. MDCI introduces an individualized approach to patient care with the aim of accelerating the availability of new therapeutic options. Continued feedback is solicited from patients on study design and the Gather Share Know hub through timed questionnaires to further enhance the patient experience. This study (NCT04772053) is funded by GlaxoSmithKline (GSK). Clinical trial information: NCT04772053.
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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 | 0.206 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.014 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".