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Utilization of online crowdfunding for alternative cancer treatments at home and abroad.

2020· article· en· W3029400833 on OpenAlexaboutno aff
J.D. Gruhl, John Peterson, Sydney Davis, Jaxon Olsen, Matthew Parsons, Benjamin H. Kann, Gregory J. Stoddard, Skyler B. Johnson

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerCancerColorectal cancerRadiation therapyLung cancerInternal medicineFamily medicineSurgery

Abstract

fetched live from OpenAlex

e14044 Background: The use of alternative cancer treatments has been associated with decreased survival. However, little is known about the types of individuals who seek out these therapies, the alternative therapies pursued, and the associated costs. We utilized GoFundMe campaigns to characterize the types of patients who sought alternative cancer treatments, the types of therapies pursued, and the associated costs. Methods: We queried GoFundMe ( www.gofundme.com ) for English language campaigns using the term “alternative cancer treatment” using custom code for web scraping on 10/25/2019. We identified 1000 campaigns between 2011-2019, of which, 795 had received donations and were used for analysis. We studied each individual campaign in detail and extracted relevant information. Results: The majority of patients were female (63.5%). The most common cancer types were breast (25.3%), colorectal (10.8%), and lung (5.5%) cancer. Of patients reporting cancer stage, 79.3% had stage IV disease. Of those reporting prior cancer treatment history, 34.8% had never undergone traditional cancer treatment; 42.1%, 46.8%, and 26.6% had undergone prior surgery, chemotherapy, and radiotherapy, respectively. The most common proposed alternative treatments were vitamins and minerals (23.4%), herbs and botanicals (16.1%), special diets (13.0%), supplements (10.8%), heat or light therapy (7.8%), IV infusions (7.8%), homeopathic/naturopathic therapies (7.2%), and hyperbaric oxygen therapy (5.9%). Among all campaigns, a total of $36,394,110 was requested and a total of $8,601,759 (23.9%) was raised. The median campaign fundraising goal was $25900 (US dollars; Interquartile range [IQR] $1000 – $50000); the median amount donated was $5805 (IQR $2595 – $12580). These costs include travel as 629 (79.1%) campaigns were for patients residing in the USA, 70 (8.8%) in Europe, 62 (7.8%) in Canada, and 34 (4.3%) in other regions, from which, 54.3% of patients stated that they planned to travel internationally—most commonly to Mexico—for their alternative cancer treatments. Conclusions: Millions of dollars have been requested and raised between 2011 and 2019 for alternative cancer treatments. The majority of patients sought treatment at alternative clinics internationally, were females, had stage IV disease and primary tumors of the breast, colon/rectum or lung, who had previously undergone traditional cancer therapies, and highlight a group of patients where improved communication/education between providers and patients appears to be needed.

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)0.002
metaresearch head score (Gemma)0.013
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 score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.003

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 head0.637
GPT teacher head0.663
Teacher spread0.026 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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