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Record 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 on 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

Bibliographic record

VenueCurrent Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiseaseFamily medicineSocioeconomic statusCancerStage (stratigraphy)Breast cancerHealth carePandemicDemographicsInternal medicineCoronavirus disease 2019 (COVID-19)DemographyPopulationEnvironmental healthInfectious disease (medical specialty)

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.427
GPT teacher head0.438
Teacher spread0.011 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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