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2012· article· en· W2563253766 on OpenAlexaffabout
Naaz Kapadia, Vera Zivanovic, Popovic Milos, Verrier Molly, Lorna Lo, Gagnon Dany, Ditor David, Rachel Brosseau, Totosy de Zepetnek Julia, Gainforth Heather, Latimer-Cheung Amy, Peter Athanasopoulos, M Kathleen, Flett Heather, Brown Jacquie, Anna Kras‐Dupuis, Laramée Marie-Thérèse, Valerie LeMay, Carol Y. Scovil, Hsieh Jane, Guy Kristina, Noonan Vanessa, Walden Kristen, Colleen McMillan, Milligan Jamie, Bauman Craig, McDonald Sarah, Hagen Chris, Karen Campbell, Houghton Pamela, Chris Fraser, David Keast, Titus Laura, Smith Joanne, James Kylie, Krassioukov Andrei, Townson Andrea, Le Nobel Gavin, Whiteneck Gale, Gassaway Julie, O'Connell Colleen, Burns Anthony, Buren Rob, A. Koempel Jeffrey, Swaine Jillian, Erin Cherban, Amir Rasheed, Wolfe Dalton, Figley Sarah, Karadimas Spyros, Salewski Ryan, Kajana Satkunendrarajah, Wilcox Jared, Michael G. Fehlings, Rachid Aïssaoui, Cyril Duclos, Nadeau Sylvie, Preuss Richard

Bibliographic record

VenueJournal of Spinal Cord Medicine · 2012
Typearticle
Languageen
FieldEngineering
TopicHuman auditory perception and evaluation
Canadian institutionsMcGill UniversityUniversité du QuébecSunnybrook Health Science CentreDalhousie UniversityUniversity of British ColumbiaWilfrid Laurier UniversityCentre for Family MedicineUniversity of WaterlooPraxis Spinal Cord InstituteOntario Neurotrauma FoundationCanadian Physiotherapy AssociationMcMaster UniversityLawson Health Research InstituteBrock UniversityWestern UniversityUniversity of VictoriaToronto Rehabilitation InstituteUniversity of New BrunswickInternational Collaboration On Repair DiscoveriesUniversité de MontréalQueen's UniversitySt Joseph's Health CareUniversity of TorontoInstitut de Readaptation Gingras Lindsay de Montreal
Fundersnot available
KeywordsMedicineComputer sciencePhysical medicine and rehabilitationPsychology

Abstract

fetched live from OpenAlex

Learning Objectives 1. Define FES.2. Identify parameters of FES as appropriate for individual patient/patient populations.3. Understand the clinical application of FES.4. Identify indications and c...

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.296
Threshold uncertainty score0.423

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0050.003
Open science0.0030.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.7040.539

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 head0.105
GPT teacher head0.356
Teacher spread0.251 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations1
Published2012
Admission routes2
Has abstractyes

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