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Record W1973176389 · doi:10.1080/13803390601161166

Verbal fluency, Trail Making, and Wisconsin Card Sorting Test performance following right frontal lobe tumor resection

2007· article· en· W1973176389 on OpenAlexafffund
Patrick S. R. Davidson, Fu Gao, Warren Mason, Gordon Winocur, Nicole D. Anderson

Bibliographic record

VenueJournal of Clinical and Experimental Neuropsychology · 2007
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsTrent UniversityUniversity of TorontoPrincess Margaret Cancer CentreHealth Sciences CentreSunnybrook Health Science CentreBaycrest Hospital
FundersCanadian Institutes of Health Research
KeywordsWisconsin Card Sorting TestFrontal lobePsychologyVerbal fluency testLesionFluencyNeuropsychological testTrail Making TestTemporal lobeNeuropsychologyAudiologyNeuroscienceCognitionMedicinePsychiatry

Abstract

fetched live from OpenAlex

Three commonly used clinical tests of frontal-executive function are verbal fluency, the Trail Making Test, and the Wisconsin Card Sorting Test, but few lesion studies of regional specificity within the frontal lobe (FL) exist for them. We examined 20 patients with right FL tumor resection, and mapped their damage to explore brain-behavior relations with greater precision. Across tests, the patients performed poorly and they also showed a deficit in switching but not clustering in verbal fluency. Within the right FL, however, we found none of the regional differences reported in studies of mixed-etiology FL patients, possibly due to the gradual neural reorganization that can occur with brain tumors. We discuss the importance of etiology in examining brain-behavior relations.

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)0.003
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.405
Teacher spread0.367 · 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

Citations68
Published2007
Admission routes2
Has abstractyes

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