MétaCan
Menu
Back to cohort
Record 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 on OpenAlexaboutno aff
Naveen Pemmaraju, Theresa Clementi, Wei Qiao, Susan K. Peterson, Vicky Zoeller, Andrew Schorr, Srđan Verstovšek

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePopulationSocial mediaThe InternetDiseaseFamily medicineInternal medicineWorld Wide WebComputer scienceEnvironmental health

Abstract

fetched live from 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.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBloodSame topicChronic Myeloid Leukemia TreatmentsFrench-language works237,207