Abstract A016: Empowering ovarian cancer patients using CancerStop: A webapp for integrating survivorship data, clinical trials, and more features for improved outcomes
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,072 | 0,023 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».