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Enregistrement W2988746454 · doi:10.1182/blood-2019-129545

Myeloproliferative Neoplasm (MPN) Patient Online Questionnaire: Assessing Patients' Disease Knowledge in a Rare Hematologic Malignancy in the Modern Digital Information Era

2019· article· en· W2988746454 sur OpenAlexaboutno aff
Naveen Pemmaraju, Theresa Clementi, Wei Qiao, Susan K. Peterson, Vicky Zoeller, Andrew Schorr, Srđan Verstovšek

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicinePopulationSocial mediaThe InternetDiseaseFamily medicineInternal medicineWorld Wide WebComputer scienceEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Background: The internet enables patients with rare cancers to have access to information, increase understanding of their disease, and garner support, all from the comfort of their own environment, without being restricted to a physical clinic space. There is growing use of the internet and social media sites among patients with rare blood cancers, most notably MPNs. Little is known about MPN patients' understanding of their own disease, and their use of online and social media resources to gain more information. Addressing this important gap could facilitate improved access to accurate informational resources about rare, complex cancers, such as MPN. Objective: Our primary aim was to evaluate knowledge and awareness of MPN in a sample of MPN patents in the Patient Power online information and support community. Methods: We developed a 38-item online questionnaire that assessed MPN knowledge and awareness, demographic and clinical characteristics, and use of online resources regarding MPN. The study population included patients who participated in the Patient Power online community and who self-identified with MPN. It is estimated that N=4,314 subscribers (no charge/free to users) self-identify as patients or caregivers with MPNs. Patient Power distributed the study questionnaire to its subscribers using an online survey platform, with an invitation to complete it if one self-identified as having an MPN diagnosis. Feasibility of administering the questionnaire was determined in a pilot sample of n=20 respondents (design phase), which was then analyzed by rigorous bio-statistical review, then allowed to continue to N=433 more respondents (expansion phase). Results: Between March to July, 2019, n=453 completed the questionnaire, including 74% female. 37% non-USA residents (1-Canada, 2-Australia, 3-UK), and 94% Caucasian. 53% reported receiving their care at a major cancer center. 58% were diagnosed with MPN between age 51-70 years. MPN subtypes were: 34% PV, 34% ET, 28% MF, 3% other/write-in, 1% don't know. Molecular subtypes included: 74% JAK2; 12% CALR; 4% MPL. 5% triple negative. A high percentage (72%) reported not being aware of additional mutations (ASXL1, etc), and 27% said their physician did not provide their risk stratification. In terms of family history of MPNs, 12% reported one or more affected family members, and 24% reported one or more family members with other blood cancers/disorders (including lymphoma, leukemia, myeloma) other than MPNs. 87% never participated in a clinical trial; of those who have, the two most common ways respondents learned about clinical trials was: from their physician; or, from a conference/meeting/organized MPN event or an online platform/social media. The survey group frequently engaged in online research, as 89% reported that using internet/online resources allowed them to look up information about MPN therapies prior to or in between doctor visits. Among The most commonly used online mediums were: 1) facebook (59%); 2) Google/Google+ (42%); YouTube (33%); and, 4) blogs (26%). Only 4% reported using Twitter. When asked if survey respondents would be willing to participate in a de-identified MPN patient registry/central database for clinical research, 95% of those surveyed responded yes. Conclusions: While our MPN patient sample cohort reported actively using online resources to seek information about their disease and treatment, results showed many gaps in basic knowledge about MPN. Based on this information, innovative proposals can be put forward to augment the patient experience and understanding of their MPN with more online educational tools, handouts/information packets in physician offices, improved approaches to educate physicians and their patients about basics of MPN diagnosis, staging, and basic and advanced molecular mutational assessments. Additionally, our findings suggest an important difference in online and social media habits of physicians compared to patients with regards to medical information and dissemination: physicians and investigators are rapidly adopting Twitter as their preferred medium for sharing medical knowledge; however patients may prefer other mediums such as Facebook, Google, or YouTube. This finding suggests that MPN educational campaigns should be designed in more personalized ways, in order to aim to fit a variety of online platforms to maximize reach and impact for patients with MPN. Disclosures Pemmaraju: sagerstrong: Research Funding; affymetrix: Research Funding; incyte: Consultancy, Research Funding; mustangbio: Consultancy, Research Funding; Daiichi-Sankyo: Research Funding; plexxikon: Research Funding; novartis: Consultancy, Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; cellectis: Research Funding; celgene: Consultancy, Honoraria; samus: Research Funding; abbvie: Consultancy, Honoraria, Research Funding. Clementi:patient power: Employment. Schorr:patient power: Employment. Verstovsek:Astrazeneca: Research Funding; Ital Pharma: Research Funding; Protaganist Therapeutics: Research Funding; Constellation: Consultancy; Pragmatist: Consultancy; CTI BioPharma Corp: Research Funding; Genetech: Research Funding; Blueprint Medicines Corp: Research Funding; Novartis: Consultancy, Research Funding; Sierra Oncology: Research Funding; Pharma Essentia: Research Funding; Incyte: Research Funding; Roche: Research Funding; NS Pharma: Research Funding; Celgene: Consultancy, Research Funding; Gilead: Research Funding; Promedior: Research Funding.

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)0,001
score de la tête « metaresearch » (Gemma)0,004
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 enseignants0,005
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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.

Tête enseignante Opus0,007
Tête enseignante GPT0,243
Écart entre enseignants0,236 · 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

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

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