Regional disparities in cancer biomarker knowledge and access across Canada.
Notice bibliographique
Résumé
e13547 Background: Clinical trials are essential to the advancement of cancer therapies, yet accrual rates remain low. Multiple challenges contribute to the less than 5% enrollment rate of patients onto clinical trials. The Clinical Trials Navigator (CTN) program identified a key factor as the lack of biomarker knowledge across Canada. Methods: Between May 2024 and October 2024, an electronic survey was conducted among Canadian oncologists to assess the knowledge and accessibility of biomarkers in different regions across the country. A comprehensive biomarker list approved for funding by Cancer Care Ontario was used as a reference standard. Physicians were asked to identify accessible biomarkers from this list. Results: A total of 36 physicians responded to the survey, predominantly from Ontario (21), followed by British Columbia (7), Alberta (3), Manitoba (2), Québec (1), Nova Scotia (1), and Newfoundland and Labrador (NFL) (1). Regional biomarker knowledge varied. ER, PR and HER2 for breast cancer were reliably identified by 18/18 physicians. Colorectal cancer biomarkers also displayed high levels of knowledge and accessibility, with 16/16 physicians reporting awareness of MLH1, MSH2, and MSH6. Lung, hematological, and pancreatic cancers were also well represented. In contrast, biomarker knowledge and accessibility for adrenal, penile, and stomach cancers were substantially lower. Among these three cancer types, 11 physicians only identified 1 (HPV) out of the 6 available biomarkers (EBER for stomach, HPV for penile, MLH1, MSH2/6, PMS2 for adrenal), highlighting gaps in advanced testing knowledge. Geographic disparities in biomarker knowledge and access revealed significant variability across Canada. NFL reported the highest accessibility (86%), although this was based on one physician respondent, limiting generalizability. Ontario had the largest number of respondents (21) and reported an overall knowledge rate of 60%. This reflects educational gaps rather than accessibility, as all biomarkers in the survey were accessible. British Columbia (67%), Manitoba (77%), and Nova Scotia (85%) also demonstrated notable knowledge and accessibility, albeit with smaller sample sizes. Conversely, some provinces, including Alberta (54%), displayed the least overall biomarker knowledge and accessibility. Conclusions: Our survey identified significant physician-reported regional disparities in biomarker accessibility and knowledge across Canada. While certain biomarkers, such as those for breast and colorectal cancers, are reported to be widely accessible, gaps are evident in biomarker testing for rare cancers. Our findings illustrate the need for targeted educational initiatives and improved resource allocation to ensure equitable biomarker access nationwide. Enhanced knowledge and accessibility to biomarker testing can improve clinical trial enrollment rates, ultimately advancing cancer care outcomes.
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
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 ».