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Record W2047333481 · doi:10.2217/nmt.13.38

Easing the Strain of Movement Disorders: From Translational and Clinical Science to Rehabilitation Strategies

2013· article· en· W2047333481 on OpenAlexaffabout
Quincy J. Almeida

Bibliographic record

VenueNeurodegenerative Disease Management · 2013
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMedalGlobePolitical scienceLibrary scienceManagementMedia studiesGerontologyMedicineHistorySociologyArt historyOphthalmology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.314
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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