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Record W2160241149 · doi:10.1109/ccece.1993.332458

Computer activated test of tactile neglect and extinction (CATTNE)

2002· article· en· W2160241149 on OpenAlexaff
C. Hershler, Tali A. Conine, H. Deo, Bruce B. Forster

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNeglectStimulus (psychology)Physical medicine and rehabilitationRehabilitationStroke (engine)Extinction (optical mineralogy)PsychologyAudiologySensory systemUnilateral neglectMedicineNeuroscienceCognitive psychologyPsychiatryBiologyEngineering

Abstract

fetched live from OpenAlex

Stroke (CVA) is a major cause of disability among older persons. Even after costly and extensive rehabilitation, the majority of stroke survivors remain functionally affected. In the absence of any sensory deficits, many stroke patients are unable to recognize a stimulus (e.g. touch) with eyes occluded or fail to recognize it when simultaneously stimulated by a second stimulus. These dysfunctions called "neglect" and "extinction" may contribute significantly to their disabling condition and could be dangerous (e.g. as when ambulating). At present, physicians and therapists do not have a reliable diagnostic tool to detect tactile neglect and extinction in CVA and in similar patients (e.g. head-injured). The authors' study was aimed at the application of computer technology to develop a simple test to assess these deficits and to evaluate various therapies which may contribute to patient recovery.>

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.227
Teacher spread0.201 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2002
Admission routes1
Has abstractyes

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