Abstract B024: Feasibility of survey-based data collection in a diverse colorectal cancer cohort: Early-onset vs. average-onset
Notice bibliographique
Résumé
Abstract BACKGROUND: The incidence of early-onset colorectal cancer (EOCRC) is rising, yet a robust model of underlying factors across diverse populations is unknown. Most studies have examined a single factor or were performed in non-diverse populations leaving a gap in understanding of how these factors interact. The collection of comprehensive, patient-reported data across different populations in CRC will enable a deeper understanding of these associations to inform early intervention strategies. METHODS: A pilot survey study was conducted at UT Southwestern Simmons Comprehensive Cancer Center (SCCC) and its affiliated safety-net hospital, Parkland Health and Hospital System (PHHS). The objective was to evaluate the feasibility of a multidomain survey in colorectal cancer patients. Eligible patients (≥18 years, stage I–IV adenocarcinoma, diagnosed within 12 months) completed baseline surveys in English or Spanish on demographics, lifestyle, symptom burden, nutrition (Dietary History Questionnaire), quality of life (EORTC QLQ-30, CR29), and financial toxicity (COST-FACIT). Surveys were administered in REDCap at baseline and three months follow-up. The primary endpoint was survey completion; feasibility was assessed by recruitment, participation, and completion rates. Descriptive comparisons were made between EOCRC (<50 years) and average-age onset colorectal cancer (AOCRC; ≥50 years). RESULTS: From March 2024 to April 2025, 66 patients were approached, and 60 (91%) consented; all completed the baseline survey (100%), confirming feasibility across academic and safety-net settings. However, for the three-month follow-up survey participation decreased by 50%. The cohort was evenly distributed by sex (48% female, 52% male) and site (50% Parkland, 50% UTSW). Participants were diverse (42% Hispanic, 18% Black, 42% Non-Hispanic White) with variable socioeconomic status: 38% reported income <$35,000, 23% >$100,000, 28% were unable to work, and 23% were employed. Hospital utilization differed: 96% of Non-Hispanic Whites were treated at SCCC, while most Hispanic and Black patients were seen at PHHS. Parkland Financial Assistance was reported by 77%, highest among Hispanic patients. Nineteen patients (32%) had EOCRC (median age 42, range 30–48) and 41 (68%) had AOCRC (median age 64, range 51–82). EOCRC patients were more often Hispanic (58% vs. 32%), treated at Parkland (58% vs. 46%), and received assistance more frequently (47% vs. 34%). CONCLUSION: This pilot study confirms that comprehensive, survey-based data collection is feasible in a racially, ethnically, and financially diverse CRC cohort. These preliminary findings highlight the clinical and sociodemographic differences of EOCRC and AOCRC across distinct groups. Future work will expand longitudinal follow-up, incorporate electronic health record data, and leverage tumor registry phenotypes to enable low-touch, systematic patient recruitment for a more robust sample at current institution, and nationally. Citation Format: Citlalli Lopez, L. Joseph Su, Luis Gonzalez, Yu-Lun Liu, Rasmi Nair, Lindsay Cowell, Emina Huang, Syed M. Kazmi. Feasibility of survey-based data collection in a diverse colorectal cancer cohort: Early-onset vs. average-onset [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B024.
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 | 0,030 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».