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Drawing-from-Memory in Constructional Apraxia: Effects of Focal Cortical Lesions on Performance

2015· article· en· W4239929363 on OpenAlexaff
Simon M. McCrea, Debbi L. Chomiak

Bibliographic record

VenueJournal of Psychology and Psychotherapy Research · 2015
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsUniversity of New BrunswickUniversity of Saskatchewan
Fundersnot available
KeywordsApraxiaNeuropsychologyPsychologyConcordanceAudiologyCognitionCognitive psychologyPhysical medicine and rehabilitationAphasiaMedicinePsychiatry

Abstract

fetched live from OpenAlex

Constructional apraxia is impairment in activities such as building, assembling, and drawing. In this paper data is presented on the performance of left and right hemisphere single focal contiguous stroke lesion participants on drawing tasks of houses, trees, and persons. In this study, 41 participants completed a comprehensive neuropsychological battery including the House-Tree-Person Drawing Task. The drawings were then rated as exhibiting signs of neurological impairment or non-impairment by three evaluators that were blinded to the medical condition of the patient. The neuropsychological evaluator did not rate the drawings. There was 72% agreement between the three evaluators of the 123 drawings in their dichotomous ratings of constructional apraxia drawings. Both a group mean and case series analysis was used to examine the data and some patterns of concordance with a detailed cognitive neuropsychological model of constructional apraxia was found.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.117
GPT teacher head0.421
Teacher spread0.304 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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