Crowdfunding for respiratory research: a new frontier for patient and public engagement?
Bibliographic record
Abstract
The rapid expansion of social media has broadened the number of crowdfunding platforms available today. This phenomenon should be seen in the context of a long tradition of appealing to the public for financial support. Modern crowdfunding initiatives have developed into a significant source of funding, garnering an estimated US $5 billion annually, with proceeds projected to expand to an annual $100 billion by 2020 [1]. Websites such as Kickstarter.com allow users to pool the resources of many contributors, funding predefined initiatives [2] with incentives ranging from merchandise or symbolic gifts, to acknowledgement of project contribution [1]. Furthermore, there has been an interest in research-specific platforms to act as an adjunct to or replacement of traditional funding sources [3]. Entities such as Experiment.com offer a platform to seek crowdfunding support ranging from basic science initiatives [3] to clinical trials. A recent systematic search identified 20 clinical trials funded via crowdfunding, with eight out of 13 completed campaigns reaching their funding goals, the largest funding allocation reaching a total of $3 113 000 [4], underscoring the financial implications of crowdfunding. Crowdfunding is a novel mechanism of public involvement that has profound implications on respiratory research
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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 | 0.017 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.070 | 0.052 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".