#FTDQuickQuestions: An FTD Disorders Registry (FTDDR) Monthly Engagement Survey to Gather Real‐time Insights to Inform Research
Bibliographic record
Abstract
Abstract Background Frontotemporal degeneration is a group of rare brain diseases that cause progressive changes to behavior, personality, language, and movement with onset typically before age 60. There are no treatments or cures. FTDDR is an international, web‐based registry (participants n>5,700) that facilitates clinical trial enrollment by collecting disease insights, summarizing data, and mobilizing potential research volunteers. FTDDR uses various data collection and engagement tools to interact with participants and the community at large. Quick Questions was concepted as a mechanism to gather real‐time, cross‐sectional, anonymous data from a broad response‐base not limited by Registry participation. Method #FTDQuickQuestions campaign incorporates social, email, and web to reach members and visitors to collect their data, strengthen existing relationships, connect with people who could benefit from Registry services, and inform research. Each month a pre‐approved question was posted to Facebook and Twitter, linking to a webform with polling options and collected for 2 weeks. Respondent’s location is indexed through IP address. Additionally, the question was emailed to FTDDR participants with brief highlights of the previous survey and a link to view results for it and prior surveys. Full summaries were posted as weblogs. Result The survey was launched in June 2022 with the question: Have you tried to get genetic testing for FTD over the last 3 years? Questions have related to FTD research participation, age of diagnosis, age first symptom appeared, where diagnosed person lived. At least one other demographic question is asked to characterize the respondent, including gender or relationship to FTD‐diagnosed person. Number of respondents for the first 7 questions ranged between 555‐857; monthly average 735. Except the initial survey, >75% of responses were received within 24 hours of email. Responses reflect 41 countries; including all 50 U.S. states and the District of Columbia; and 10 Canadian provinces/territories. Majority are women (62%); a third men (35%); 3% declined providing gender. Conclusion FTDDR’s #FTDQuickQuestions is a successful monthly engagement mechanism that enables quick, timely responses to questions about this disease and the people affected by it. While related questions cannot be compared nor connected, this valuable information serves as a cross‐sectional snapshot to inform FTD 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 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".