Rapid Web-Based Recruitment of Patients With Psoriasis: Multinational Cohort Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Wide-ranging patient recruitment not restricted to the location of the investigator will provide a better representation of the patient population in clinical studies. OBJECTIVE Our goal was to assess the feasibility of a broad web-based recruitment strategy in an 8-week observational study of 500 study participants with psoriasis and healthy controls from locations remote from the investigator and to assess the cost associated with each participant. METHODS A decentralized team in Denmark recruited patients with psoriasis and healthy controls using Google and Facebook advertisements and posts to Facebook groups. All individuals were screened via the internet, and patients diagnosed with psoriasis were included. Questionnaires regarding itch and sleep were completed by both groups at inclusion, week 4, and week 8. RESULTS During a 2-week recruitment period, 12,887 unique advertisement views were registered, and 839 participants were enrolled, of which 507 completed the study (220 with psoriasis and 287 healthy controls) with a retention rate of 60.4%. Participants were recruited from 11 different countries on 4 separate continents, mainly from the United States, Canada, and the United Kingdom. The recruitment rate was 59.9 participants per day, and the conversion rate was 57.2%. Recruitment costs were US $13 per enrolled participant and US $22 per participant completing the study. CONCLUSIONS It is feasible and rapid to recruit a large number of participants from locations different from the investigator and to retain patients in an observational study with no visits to a clinical site at low costs.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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.
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".