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Enregistrement W4385802347 · doi:10.3390/curroncol30080551

Exploring the Use of a Digital Platform for Cancer Patients to Report Their Demographics, Disease and Therapy Characteristics, Age, and Educational Disparities: An Early-Stage Feasibility Study

2023· article· en· W4385802347 sur OpenAlexvenueno aff
Dimitra Galiti, Helena Linardou, Sofia Agelaki, Athanasios Karampeazis, Nikolaos Tsoukalas, Amanda Psyrri, Michalis V. Karamouzis, Konstantinos N. Syrigos, Alexandros Ardavanis, Ilias Athanasiadis, Eleni Arvanitou, Stavroula Sgourou, Αναστασία Μαλά, Christos Vallilas, Ioannis Boukovinas

Notice bibliographique

RevueCurrent Oncology · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueCancer survivorship and care
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineDiseaseFamily medicineSocioeconomic statusCancerStage (stratigraphy)Breast cancerHealth carePandemicDemographicsInternal medicineCoronavirus disease 2019 (COVID-19)DemographyPopulationEnvironmental healthInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: The increasing burden of cancer, the development of novel therapies, and the COVID-19 pandemic have made cancer care more complex. Digital innovation was then pushed toward developing platforms to facilitate access to cancer care. Age, education, and other disparities were, however, shown to limit the use of the digital health innovation. The aim of this early-stage feasibility study was to assess whether Greek cancer patients would register at CureCancer and self-report their demographics, disease and therapy characteristics, and socioeconomic issues. The study was organized by the Hellenic Society of Medical Oncology. METHODS: Patients from nine cancer centers were invited to register on the CureCancer platform and complete an anonymous questionnaire on demographics, disease and therapy characteristics, and socioeconomic issues. Patients were also encouraged to upload, in a secure area for them, their medical files and share them with their physicians. They were then asked to comment on their experience of registration and how easy it was to upload their medical files. RESULTS: Of the 159 patients enrolled, 144 (90.56%) registered, and 114 of those (79.16%) completed the questionnaire, suggesting that the study is feasible. Users' median age was 54.5 years, and 86.8% of them were university and high school graduates. Most patients (79.8%) reported their specific type of cancer diagnosis, and all reported their therapy characteristics. Breast and lung cancers were the most common. A total of 87 patients (76.3%) reported being on active cancer therapy, 46 (40.4%) had metastatic disease, and 51 (44.7%) received supportive care medications. Eighty-one (71.05%) patients received prior cancer therapies, and twenty-seven recalled prior supportive care medications. All patients reported visiting non-oncology Health Care Professionals during the study. Nineteen of 72 (26.39%) patients who worked prior to cancer diagnosis changed work status; 49 (42.98) patients had children under 24 years; and 16 (14%) patients lived alone. Nine (7.9%) patients were members of patient associations. Registration was "much/very much" easy for 98 (86.0%) patients, while 67 (58.8%) had difficulties uploading their files. Patients commented on the well-organized data access, improved communication, feeling safe, medication adherence, interventions from a distance, and saving time and money. Over 80% of patients "preferred the digital way". DISCUSSION: A total of 114 patients succeeded in registering on the digital platform and reporting their demographics, disease and therapy characteristics, and socioeconomic issues. Age and educational disparities were disclosed and highlighted the need for educational programs to help older people and people of lower education use digital innovation. Health care policy measures would support patients' financial burden associated with work changes, living alone, and children under 24 years old at school or college. Policy actions would motivate patients to increase their participation in patient associations. According to the evidence DEFINED framework, the number of patients, and the focus on enrollment, engagement, and user experience, the study fulfills actionability level criterion 1.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,048

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,010
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0010,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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.

Tête enseignante Opus0,427
Tête enseignante GPT0,438
Écart entre enseignants0,011 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission1
Résumé présentoui

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