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Record W1937080858 · doi:10.1017/s1355617701777120

WMS–III performance in patients with temporal lobe epilepsy: Group differences and individual classification

2001· article· en· W1937080858 on OpenAlexaff
Nancy J. Wilde, Esther Strauss, Gordon J. Chelune, David W. Loring, Roy C. Martin, Bruce P. Hermann, Elisabeth M. S. Sherman, Michael A. Hunter

Bibliographic record

VenueJournal of the International Neuropsychological Society · 2001
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsBC Children's HospitalUniversity of Victoria
Fundersnot available
KeywordsEpilepsyTemporal lobePsychologyCognitive psychologyAudiologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

The utility of the WMS-III in detecting lateralized impairment was examined in a large sample of patients with temporal lobe epilepsy. Methods of analysis included evaluation of group means on the various indexes and subtest scores, the use of ROC curves, and an examination of Auditory-Visual Index discrepancy scores. In addition, performance on immediate and delayed indexes in the auditory and the visual modality was compared within each group. Of the WMS-III scores, the Auditory-Visual Delayed Index difference score appeared most sensitive to side of temporal dysfunction, although patient classification rates were not within an acceptable range to have clinical utility. The ability to predict laterality based on statistically significant index score differences was particularly weak for those with left temporal dysfunction. The use of unusually large discrepancies led to improved prediction, however, the rarity of such scores in this population limits their usefulness. Although the utility of the WMS-III in detecting laterality may be limited in preoperative cases, the WMS-III may still hold considerable promise as a measure of memory in documenting baseline performance and in detecting those that may be at risk following surgery.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.003
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

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

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 head0.044
GPT teacher head0.294
Teacher spread0.250 · 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 teacher head, 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

Citations66
Published2001
Admission routes1
Has abstractyes

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