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Record W2409434451

Atypical language lateralization in patients with left hippocampal sclerosis: does the hippocampus affect language lateralization?

2009· article· en· W2409434451 on OpenAlexaff
Taner Tanrıverdi, Qasim Al Hinai, Kelvin Mok, Denise Klein, Nicole Poulin, André Olivier

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMedicineLateralization of brain functionHippocampal formationHippocampal sclerosisLesionAudiologyMultiple sclerosisHippocampusTemporal lobePathologyInternal medicinePsychiatryEpilepsy
DOInot available

Abstract

fetched live from OpenAlex

AIM: To provide information related to atypical language activations (right or bilateral) in positron emission tomography in patients with left clear-cut hippocampal sclerosis. MATERIAL AND METHODS: Twelve right-handed patients who had been operated on left-sided hippocampal sclerosis and 12 right-handed normal subjects were included and the synonym generation task was used for evaluation of language lateralization. RESULTS: Atypical language activations were frequently found in the patients compared to the controls. A total of 3 (25%) subjects in the controls showed atypical activations: 2 bilateral with right and 1 bilateral with left-sided activations. There were no clear right-sided Broca activations in the control group but almost 25% of the patients showed clear right-sided Broca activations. In the patients the incidence of atypical language activations was 91.6% (11 patients). CONCLUSION: From the present study, it is clear that functional reorganization of the language-related neuronal network is modified in patients with left hippocampal sclerosis. Although the lesion is far from the primary language-related areas, atypical language lateralization is common in these patients and this should be considered in preoperative period.

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)0.001
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 score0.189
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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