Toronto dementia research alliance (TDRA) dementia clinical‐research platform: An example of research embedded into clinical care
Bibliographic record
Abstract
Abstract Background Dementia is arguably the greatest looming global public health challenge facing today’s society and represents an international crisis as the number of affected persons will triple by 2050. Contributing to morbidity of dementia is the presence of combined pathologies, such as Alzheimer’s disease (AD), cerebrovascular disease (CVD) and Parkinson’s disease (PD). To address this challenge, the TDRA ‐ a collaboration of the University of Toronto’s Faculty of Medicine with four University‐affiliated tertiary memory clinics ‐ created an electronic platform for research studies embedded in clinical care through development of a unified approach for diagnosis and charting of the natural history of pure and mixed dementias, along with the impact of co‐occurring disorders. Method A detailed clinical intake form and cognitive testing (for mild‐to‐severe impairment) were created through a collaboration among behavioural neurologists, geriatricians and geriatric psychiatrists. These measures are captured electronically at point‐of‐care and, simultaneously, the deidentified information uploaded to a central research server (see Figure 1). Result After approximately 1 year since collecting information, 1182 new patients (622 women; 560 men) have been evaluated – 119 AD; 76 concussion; 20 CBS; 8 delirium; 2 MBI, 278 MCI, 42 mixed AD and CVD; 16 PSP; 1 MSA‐P, 16 PD, 17 PD‐MCI, 21 PDD; 32 DLB; 24 bvFTD; 7 lvPPA, 13 nfPPA; 12 svPPA, 136 with primary and/or co‐morbid psychiatric disease; 165 subjective cognitive impairment; 93 VCI; 18 normal cognition; and 18 dementia NYD. Each of these patients have some demographic and clinical details, such as comorbid psychiatric and/or medical disorders, and cognitive testing obtained for research. Conclusion The TDRA Dementia Clinical‐Research Platform is a practising example of research embedded into clinical care with over 1000 new patients with diverse diagnoses evaluated in 1 year and may represent the types of dementia seen in Toronto and surrounding areas. A letter, based on the clinical intake form, is being programmed to send back to the referring physician and will enhance clinical care, along with scores and graphic display of the cognitive testing results.
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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 | 0.065 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".