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Record W2592670365 · doi:10.1080/23279095.2017.1296839

Attentional impairments following right hemisphere damage with and without hemispatial neglect: A comparative study

2017· article· en· W2592670365 on OpenAlexaff
Cristina Elizabeth Izábal Wong, Laura Damiani Branco, Charles Cotrena, Yves Joanette, Róchele Paz Fonseca

Bibliographic record

VenueApplied Neuropsychology Adult · 2017
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHemispatial neglectNeglectRight hemisphereAudiologyTask (project management)PsychologyReading (process)Physical medicine and rehabilitationDevelopmental psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

This study aimed to evaluate the performance of patients with right hemisphere damage (RHD) with or without hemispatial neglect (HN) on a cancellation task. The study involved 31 control participants and 31 patients with RHD, matched by age, education, and frequency of reading and writing habits. The numbers of omission and random errors as well as the mean time to task completion were compared between adults with and without RHD, as well as between patients with and without HN. The latter made more left-sided omission errors, and more overall omission errors, than patients with RHD and no HN. The location of the first target canceled differed between subjects with RHD and control participants, as well as between patients with and without hemineglect. The use of organized vs. disorganized search strategies did not differ between groups. Further studies are required to investigate the performance of patients with HN of different levels of severity.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.310
Teacher spread0.285 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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