Utilization and reach of the Fight Colorectal Cancer Late Stage MSS CRC Clinical Trial Finder.
Bibliographic record
Abstract
3561 Background: Colorectal cancer (CRC) remains one of the most lethal cancer killers worldwide. Recently, research has shown great strides in the treatment of MSI-H mCRC using immunotherapy, however, these treatments have not been effective in MSS patients, who make up a majority of CRC cases. Due to numerous barriers, clinical trial enrollment numbers remain as low as 9% of the eligible populations, despite the reliance of many late stage CRC patients on clinical trials for treatment. Perhaps greatest of these barriers is the lack of meaningful patient-facing clinical trial matching, making advances in MSS mCRC IO clinical research extremely slow. Methods: In May 2017, Fight Colorectal Cancer (FightCRC) launched its web-based trial finder, The Late Stage MSS Trial Finder (TF) with the late Dr. Tom Marsilije, a stage IV CRC patient and researcher, and Flatiron Health. The TF is a publicly available immunotherapy-based repository of clinical trials. An algorithm automatically codes for a subset of trials from ClinicalTrials.gov to be uploaded into the tool, and trained FightCRC advocates follow a strategic logic flow to prioritize trials of highest potential benefit and lowest risk for patients. Results: Between 30 and 100 trials are uploaded into the TF for curation each week. A total of 378 trials have been indexed in the TF to date. In February 2019, a mobile application was introduced. From May 2017 to January 2019, the tool has seen > 15,000 users, yielding 26,000 searches in 105 countries; primarily from the United States, China, the United Kingdom, Canada, and France. On average, users navigate to 2.5 pages and spend > 2.5 minutes per use. Providers are using this as a tool to find clinical trials and to discuss these options in real time. CRC patient feedback confirmed the platform functionality. Conclusions: The Trial Finder is a unique tool for MSS mCRC patients pursuing clinical trials. The success of the tool may be attributed to patient focused selection of therapies that show promise. The FightCRC late stage MSS CRC trial finder is being widely utilized, in diverse settings. With our patient curators and Medical Advisory Board, FightCRC will improve the search features and outcome tracking with user feedback. The goal for the TF is to address key barriers to patient entry into clinical trials and promote patient-provider discussions to inform decision making.
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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 | 0.033 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.127 | 0.050 |
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".