Easing the Strain of Movement Disorders: From Translational and Clinical Science to Rehabilitation Strategies
Bibliographic record
Abstract
Quincy Almeida is the Director of the Sun Life Financial Movement Disorders Research and Rehabilitation Centre at Wilfrid Laurier University (ON, Canada), one the world’s leading authorities on movement science and rehabilitation in Parkinson’s disease (PD). His research has been featured in the Toronto Star, the Globe & Mail, on CBC and CTV national news as well as features in Maclean’s magazine. He has been funded by the Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada and by a multimillion dollar grant from the Canada Foundation for Innovation, and his innovative research on PD has won several awards, including the Franklin Henry Young Scientist Award for motor control in Canada, and the Parkinson’s Society of Canada Young Investigator’s Award. More recently, he received the Polanyi Prize for Physiology and Medicine, the Queen Elizabeth’s II Diamond Jubilee medal in January 2013, and in June 2013 Almeida gave a keynote when he was honored with a North American Award, the Early Career Distinguished Scholar Award from the North American Society for the Psychology of Sport and Physical Activity organization at a conference in New Orleans (LA, USA). Almeida has spoken about his novel approach to understanding PD across the world, including in France, Italy, Brazil, Ireland, Norway, Australia and The Netherlands.
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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 | 0.036 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".