Abstract P3-22-01: The past and present of breast cancer resources: A re-evaluation of the quality of online resources in breast cancer after eight years
Notice bibliographique
Résumé
Abstract Purpose: The internet has become a central resource for cancer patients, with recent studies reporting 60% to 75% of cancer patients using online resources. However, the quality of these resources is variable, and a better understanding is needed to guide physicians in how to best support patients in their online searches. We previously evaluated the quality of online breast cancer resources in 2011. Now, nearly a decade later, we aimed to assess the present quality of online breast cancer patient information and to compare our current analysis to data collected in 2011. . Materials and Methods: A list of top 100 breast cancer websites was systematically compiled using meta-search engines Yippy, Dogpile, and Google using the search term “breast cancer”. Content accuracy and quality markers, including authorship, attribution, currency, site organization, and readability were assessed using a previously validated standardized rating tool. Results were analyzed using descriptive statistics and Fisher’s exact test. The same strategy was used in both 2011 and 2019. . Results: When comparing current data to 2011, authorship identification increased, with 34% of websites identifying an author in 2011 compared to 45% in 2019 (p=0.004). Only 31% of websites analyzed in 2019 used two or more reliable sources, while 62% had no reliable sources or no sources cited. Website disclosure and objectivity remained similar in both data sets. Twenty seven percent of websites were updated in the last 2 years in 2011 compared to 65% in 2019 (p<0.0001). From 2011 to 2019, resources with readability above grade 12 increased from 4% to 30% (p<0.0001), while websites offering educational support rose from 8% to 35% (p<0.0001). In 2019, treatment and etiology/risk factors were the most accurately covered (64% and 63% of websites, respectively). This was similar to 2011 data, which found 63% of websites to be globally accurate. Prognosis coverage increased from 18% to 33% from 2011 to 2019 (p=0.023). In 2019, survivorship was also evaluated, and found to be covered in only 24% of resources. Conclusions: Over the past 8 years, there has been some improvement in the quality of online breast cancer resources. Promisingly, websites are being updated more frequently and the educational support offered is expanding. Furthermore, there has been significant improvement in the coverage of prognosis, although this requires further progress. Unfortunately, websites are becoming increasingly challenging to understand for an average patient, and coverage of survivorship is lacking. Our study informs healthcare providers on these trends in online breast cancer resources and how to best support patients in their internet searches. Citation Format: Veronika Killow, Paris Ingledew, Julia Lin. The past and present of breast cancer resources: A re-evaluation of the quality of online resources in breast cancer after eight years [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-22-01.
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 | 0,025 | 0,132 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,014 | 0,020 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».