Crowdfunding in urology: Canadian perspective
Bibliographic record
Abstract
INTRODUCTION: Crowdfunding is becoming an increasingly used resource for patients to cover costs related to medical care. These costs can be related directly to treatments or indirectly to loss of income or travel-related costs. Little is known as to the extent of which crowdfunding is used for urological disease here in Canada. This study offers a first look at the prevalence of crowdfunding for urological disease and the factors surrounding its use. METHODS: In January 2020, we queried the GoFundMe internal search engine for fundraising campaigns regarding urological ailments. Results were categorized according to the major organs of urological disease. RESULTS: Crowdfunding campaigns are very prevalent within several areas of urology. Prostate cancer and chronic kidney disease represent the most frequent reason for campaigns. Fundraising goals and actual funds raised for malignant disease were significantly more than for benign disease. Interestingly, there was a significant portion of crowdfunding campaigns to cover costs for non-conventional treatments and transplant tourism. CONCLUSION: Crowdfunding use to help cover direct and indirect costs of medical care is becoming increasingly apparent through several facets of medicine. This study shows that this statement holds true when looking at patients with urological disease in Canada. As urologists, we need to be aware of this trend, as it highlights the often-unforeseen financial burdens experienced by our patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".