Abstract A016: Empowering ovarian cancer patients using CancerStop: A webapp for integrating survivorship data, clinical trials, and more features for improved outcomes
Bibliographic record
Abstract
Abstract Introduction: Patients with ovarian cancer often face complex treatment decisions and survivorship issues, as the onset and diagnosis usually occur at advanced stages. Timely knowledge is key to decision-making in these situations. It is thus crucial to seamlessly integrate data from disparate recognized public sources in a way that is easily accessible and comprehensible to a broader audience. CancerStop.dev is a web-based platform built on a modern framework powered by React, a JavaScript library for building dynamic and responsive user interfaces, ensuring seamless interaction and scalability across devices. The site offers several in-built interactive modules that enable patients to connect with the latest research and cures. Features: 1) Relative survival curves: Using an inbuilt regression model we developed, this unique feature combines 'age-at-diagnosis' and 'stage-at-diagnosis' based survival data from the NCI's SEER explorer. An interactive graph displays customized age survival curves for different stages of cancer spread, including local, regional, distant, and unstaged. 'Relative survival' rates for each age are modeled up to 10 years from diagnosis. This personalized approach to data presentation helps patients understand their prognosis and make informed decisions about their treatment options. 2) Ongoing trials and new cures: Users are linked to highly relevant results from ClinicalTrials.gov in the same interface. A custom search box allows filtering and narrowing results by any keyword, including mutations, new drugs, trial locations, etc. This seamless integration provides easy access and hope to be part of ongoing research and new treatment opportunities. 3) Genes and More: When genetic testing is performed and patients need to understand a gene variant's currently noted prognostic significance, they can enter specific terms to access ClinVar via NCBI. The interface is scalable to connect with other databases for a side-by-side comparison of similar variants. This is especially helpful when complementing searches for targeted therapies based on genetic profiles. 4) Approved Drugs: The feature links directly to the National Cancer Institute's list of approved drugs for ovarian and related cancers, offering detailed information on medications. This extra level of access supports patients in understanding their treatment options and drug efficacies. Impact on Patient Advocacy: CancerStop.dev greatly empowers ovarian cancer patients by seamlessly linking them to forward-looking information from reliable public sources and enhancing their ability to advocate for themselves. Combining unique features makes this a holistic resource while supporting patients throughout their cancer journey. The site remains widely visited with positive testimonials from users and physicians, highlighting the positive impact on patient advocacy. Future directions: Further enhancements will continue to expand new features to support better patient engagement and drive improved outcomes. Citation Format: Vedanth Ramji, Baladithya Muralidharan, Ganeshram Janakiraman, Natarajan Ganesan. Empowering ovarian cancer patients using CancerStop: A webapp for integrating survivorship data, clinical trials, and more features for improved outcomes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr A016.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.023 |
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".