The PPA Platform: A unique knowledge translation tool on PPA
Bibliographic record
Abstract
Abstract Background Very limited web‐based information is available for patients and families affected by Primary Progressive Aphasia (PPA). When accessible, it is often incomplete or worded in complex terminology. One of the objectives of the Research Chair on PPA – Fondation de la famille Lemaire (app‐ffl.ulaval.ca) is to educate the general population and healthcare professionals on PPA to promote earlier diagnosis and better care. We present the development of the PPA Platform, an innovative knowledge transfer tool. Method We carried out a focus group with patients and their relatives to understand their needs. We developed templates for ‘Patients/Relatives’ and ‘Health Professionals’ and adapted content and terminology for both sections. Content was then validated with experts in the field (neurologists, SLP). Videos were selected to illustrate the symptoms (eg, naming, repetition, etc.) and we included testimonials to further capture caregivers’ experience (eg, first symptoms, challenges, etc.). The French version of the platform was launched in 2020 and the English one in 2022. Result Since its inauguration, the PPA Platform has been consulted by more than 27 000 individuals in over 85 countries. Moreover, its content has been presented to approximately 500 healthcare professionals over training sessions either in person or by videoconference. Content is regularly updated and translation in other languages (Mandarin, Hindi, Spanish, Arabic and German) is ongoing. Conclusion The PPA Platform is an outstanding knowledge translation tool for patients, families and healthcare professionals. In a short amount of time, it positioned itself worldwide as a key reference on PPA.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 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".