Count Me In: patient-partnered research to address disparities for rare cancer patients
Bibliographic record
Abstract
Background: Approximately 25% of cancer patients are diagnosed with rare cancers and face unique challenges. Decentralized patient-partnered research efforts, like Count Me In provide an avenue for patients to participate in research that overcomes key barriers to address disparities in rare cancer research to accelerate discovery. Objectives: Projects in metastatic breast cancer (The Metastatic Breast Cancer Project; MBCproject) and angiosarcoma (The Angiosarcoma Project; ASCproject) highlight disparities that exist for all cancer patients and underscore those that are compounded for rare cancer patients. Design: Through Count Me In's research platform, patients visit a website to enroll in the study and complete surveys, which allows us to access their medical records and biospecimens. Clinically annotated sequencing data are de-identified and released on research platforms. Methods: MBCproject and ASCproject data were analyzed to identify differences between patients with a more common and rare cancer, respectively. The analysis included outreach strategies, patient-reported themes, and distance traveled for care. Results: As of September 28, 2023, 3742 patients have enrolled in MBCproject and 491 patients have enrolled in the ASCproject from across the United States and Canada. Outreach strategies were tailored to resource availability. Using survey information, it was observed that patients with a rare cancer (angiosarcoma) traveled longer distances to receive care than those with a more common cancer (metastatic breast cancer) for three major cancer centers. Patients with rare and common cancers highlighted different themes when asked about their disease experience. Themes like misdiagnosis and discontent with resource availability came up more often for rare cancer patients. Data sharing and collaboration in angiosarcoma research enabled rapid discoveries with clinical impact. Conclusion: Count Me In's platform has led to unprecedented data generation and findings in rare cancer through partnering with patients. Directly engaging with patients to generate and share data while emphasizing collaboration sets the foundation for a more equitable future.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".