Building a Citizen Pscientist: Advancing Patient-Centered Psoriasis Research by Empowering Patients as Contributors and Analysts
Bibliographic record
Abstract
INTRODUCTION: To design and implement a novel cloud-based digital platform that allows psoriatic patients and researchers to engage in the research process. METHODS: Citizen Pscientist (CP) was created by the National Psoriasis Foundation (NPF) to support and educate the global psoriatic disease community, where patients and researchers have the ability to analyze data. Psoriatic patients were invited to enroll in CP and contribute health data to a cloud database by responding to a 59-question online survey. They were then invited to perform their own analyses of the data using built-in visualization tools allowing for the creation of "discovery charts." These charts were posted on the CP website allowing for further discussion. RESULTS: As of May 2017, 3534 patients have enrolled in CP and have collectively contributed over 200,000 data points on their health status. Patients posted 70 discovery charts, generating 209 discussion comments. CONCLUSION: With the growing influence of the internet and technology in society, medical research can be enhanced by crowdsourcing and online patient portals. Patient discovery charts focused on the topics of psoriatic disease demographics, clinical features, environmental triggers, and quality of life. Patients noted that the CP platform adds to their well-being and allows them to express what research questions matter most to them in a direct and quantifiable way. The implementation of CP is a successful and novel method of allowing patients to engage in research. Thus, CP is an important tool to promote patient-centered psoriatic disease research.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".