Abstract 26: A novel electronic platform to improve clinical trial workflow and screening
Bibliographic record
Abstract
Abstract Background: Early-phase clinical trials are increasingly complex, with “seamless” phase I/II trial designs that include multiple parallel disease and/or biomarker-selected expansion cohorts. Challenges for participating sites in phase 1 (P1) trials include rapid identification of patients for available trial slots, high screen failure (SF) rates, and workload distribution among nurses and data coordinators. The Princess Margaret (PM) Cancer Centre Solid Tumor P1 program is the largest early-phase clinical trial program in Canada. There are currently 53 actively recruiting clinical trials, and approximately 250-300 patients are enrolled annually. To improve operational efficiency at the point of care, we designed a novel electronic platform, termed Phase One Spot Tracker (POST). Methods: An electronic, password-restricted, web-based tracker and smartphone-enabled app was created using PHP Laravel, JavaScript (iOS), and Java (Android). The intention was to replace the two previously used Excel spreadsheets, where one listed the trial information and the other included available slot allocations by trial. These spreadsheets were cumbersome to use in clinic, without the ability to search or filter. POST enabled all users to access the data from any computer or phone, increased the ease of updating information in real time, and functioned as a search engine to locate available spots more quickly. It contains specific information including brief trial description, key eligibility criteria, biopsy requirements, status of trial slots (e.g., screening, available, pending, etc.). The data security of POST is ensured by Amazon Web Services. Results: Prior to launching POST, three time-measured simulations showed that it reduced the coordinators’ workload to update trial information by 30 minutes. Currently POST is used by 65 users, including 7 physicians, 14 nurses, and 29 coordinators at the PM Cancer Centre. POST is used by investigators during a weekly P1 new patient consultation clinic to identify trial candidates as well as in weekly P1 team meetings to review all patients enrolled and approached for clinical trials. We are enhancing the POST platform to enable tracking of SF (patients who undergo study-specific screening procedures but are not enrolled in a clinical trial), timeline for replacing patients who screen fail, and reasons for SF. Conclusions: POST is an electronic, smart phone-enabled platform that can be implemented at the point of care for early-phase clinical trials. POST can also provide real-time tracking data about operational metrics (startup time, screening time, enrollment data, nursing and data coordinator workload, and screen failures) that can be used to drive quality improvement. POST can be extended to other disease site groups as well as to other institutions. This will eventually lead to an increase in work efficiency and provide a better quality of care for cancer patients. Citation Format: Ramy R. Saleh, Olivia Chan, Silvi Kuld, Patrick Marban, Anna Spreafico, Aaron Hansen, Albiruni Razak, Lillian L. Siu, Philippe L. Bedard. A novel electronic platform to improve clinical trial workflow and screening [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 26.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads 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".