MétaCan
Menu
Back to cohort
Record W4379157402 · doi:10.2196/43845

Impact of, Factors for the Success of, and Concerns Regarding Transplant Patients’ Skin Cancer Campaigns: Observational Study

2023· article· en· W4379157402 on OpenAlexvenueno aff
Erica Mark, Joseph Nguyen, Fatima Choudhary, Jules B. Lipoff

Bibliographic record

VenueJMIR Dermatology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSkin cancerDiseaseObservational studyDemographicsLogistic regressionFamily medicineCancerEnvironmental healthInternal medicineDemography

Abstract

fetched live from OpenAlex

BACKGROUND: Due to rising health care costs, patients have sought alternative ways of addressing medical expenses. In particular, transplant patients have complex and expensive medical needs-including skin cancer surveillance-that may not be fully covered by insurance. One such method of financing medical costs is by crowdsourcing through web-based platforms, most notably GoFundMe. OBJECTIVE: Previous work identified factors associated with GoFundMe campaigns' fundraising success for dermatologic diseases. We sought to characterize these factors in transplant recipients' campaigns for funds raised for covering skin cancer-related costs. These factors include demographics, campaign traits, and subjective themes. METHODS: From January to April 2022, we analyzed GoFundMe campaigns using the following search terms chosen on the basis of author consensus: "transplant skin cancer," "transplant basal cell," "transplant squamous," "transplant melanoma," and "dermatologist transplant." Demographic data were coded from campaign text or subjectively coded based on author consensus. Campaigns were read completely by 2 independent coders and associated with up to 3 different themes. Linear regression was performed to examine the qualities associated with success, which was defined as funds raised when controlling for campaign goals. Logistic regression was used to examine qualities associated with extremely successful campaigns, defined as those raising funds over 1.5 times the IQR. RESULTS: Across 82 campaigns, we identified several factors that were associated with fundraiser success. Patients who experienced complications during infectious disease treatment, those who received a pancreas transplant, or those who died from their disease raised significantly more money. Patients older than 61 years raised significantly less money. Extremely successful campaigns (>US $20,177) were associated with campaigners who emphasized a disability from their disease, those who were reluctant to ask for help, or those who died due to their disease. CONCLUSIONS: Demographic and thematic factors are associated with transplant patients' skin cancer-related fundraising success, favoring those who are younger, in more extreme situations, and appear reluctant to ask for help; these findings are consistent with those of previous studies. Additionally, transplant patients have complex and expensive dermatologic needs that may not be fully covered by insurance, as reflected in their GoFundMe campaigns. The most commonly mentioned reasons for fundraising included living expenses or loss of income, inadequate or no insurance, and end-of-life costs. Our findings may inform transplant patients how to maximize the success of their campaigns and highlight gaps in health care coverage for skin cancer-related costs. Limitations include the possibility for misclassification due to the data abstraction process and limiting data collection to fundraisers available on GoFundMe while excluding those on other websites. Further research should investigate the ethical implications of crowdfunding, financial needs of this patient population, and potential ways to improve access to routine skin cancer surveillance among patients receiving transplants.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.104
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

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

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 head0.272
GPT teacher head0.498
Teacher spread0.226 · 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 teacher head, 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJMIR DermatologySame topicSocial Media in Health EducationFrench-language works237,207