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Record W2075357267 · doi:10.1310/tsr1405-56

The Rehabilitation of Patients Recovering from Brainstem Strokes: Case Studies and Clinical Considerations

2007· article· en· W2075357267 on OpenAlexaff
Elizabeth Kruger, Robert Teasell, Katherine Salter, Norine Foley, Chelsea Hellings

Bibliographic record

VenueTopics in Stroke Rehabilitation · 2007
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsSt Joseph's Health CareWestern UniversityLawson Health Research Institute
Fundersnot available
KeywordsDysarthriaRehabilitationStroke (engine)MedicineDysphagiaBrainstemAtaxiaNeurorehabilitationDiplopiaPhysical medicine and rehabilitationPhysical therapyAudiologySurgeryPsychiatry

Abstract

fetched live from OpenAlex

An estimated 15% of all patients admitted to stroke rehabilitation units experience a brainstem stroke. Two case studies are presented to illustrate some of the difficulties encountered in the rehabilitation of these individuals. Unlike hemispheric stroke, the characteristic consequences of brainstem stroke include ataxia, dysarthria, and diplopia. Additionally, individuals with brainstem stroke may suffer from severe dysphagia and may require enteral feedings. Unlike the rehabilitation of patients with hemispheric stroke, where there is an impressive and relatively comprehensive research literature, there has been surprisingly little research published on the rehabilitation of patients with brainstem stroke despite the fact they represent a significant number of patients admitted to stroke rehabilitation units.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0070.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.064
GPT teacher head0.462
Teacher spread0.398 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations30
Published2007
Admission routes1
Has abstractyes

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