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Record W2158671094 · doi:10.3109/02703181.2012.750411

The Effect of Visuospatial Neglect on Functional Outcome and Discharge Destination: An Exploratory Study

2012· article· en· W2158671094 on OpenAlexaff
Rebecca Timbeck, Sandi J. Spaulding, Lisa Klinger, Jeffrey D. Holmes, Andrew M. Johnson

Bibliographic record

VenuePhysical & Occupational Therapy In Geriatrics · 2012
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsWestern University
Fundersnot available
KeywordsRivermead post-concussion symptoms questionnaireNeglectBerg Balance ScaleRehabilitationFunctional Independence MeasurePsychologyPhysical medicine and rehabilitationActivities of daily livingExploratory researchPhysical therapyClinical psychologyAudiologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

Background and Purpose: The purpose of this study was to evaluate the effect of visuospatial neglect on functional outcome and discharge destination in individuals with right brain damage. Methods: Sixteen subjects agreed to participate in the study and 6 of these subjects demonstrated visuospatial neglect. During two different data collection periods, participants were evaluated with the Rivermead Behavioral Inattention Test, the Functional Independence Measure (FIM), the Mini-Mental State Examination, the Berg Balance Scale, and the Chedoke-McMaster Impairment Inventory. Results: Admission and discharge FIM scores were significantly lower for subjects with visuospatial neglect. The discharge destination for the group with visuospatial neglect demonstrated a trend toward supported living (i.e., long-term care). The groups did not differ significantly in onset to rehabilitation admission interval or length of rehabilitation stay. Conclusions: The presence of visuospatial neglect may predict the need for greater amounts of caregiver assistance at discharge.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.349
Teacher spread0.288 · 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 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

Citations5
Published2012
Admission routes1
Has abstractyes

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