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Record W2790722325 · doi:10.1590/2317-1782/20182016225

Caracterização das afasias na hemorragia subaracnóidea aneurismática

2018· article· pt· W2790722325 on OpenAlexaboutno aff
Deborah Karolyne Mendes Maranhão, Moysés Loiola Ponte de Souza, María Lúcia Gurgel da Costa, Ana Cláudia de Carvalho Vieira

Bibliographic record

VenueCoDAS · 2018
Typearticle
Languagept
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials science

Abstract

fetched live from OpenAlex

PURPOSE: Characterize the profile of aphasic syndromes determined by aneurysmal subarachnoid hemorrhage (aSAH) of the left middle cerebral artery (LMCA). METHODS: An analytical, retrospective, cross-sectional study was conducted using a database of cognitive assessments of 193 patients with aSAH admitted to Hospital da Restauração between March 2007 and November 2009. Of these, a total of 26 patients with aSAH in the LMCA territory confirmed by digital angiography were selected. Aphasia was assessed through the Montreal-Toulouse Language Assessment Alpha Version Protocol (Alpha Version) and the CERAD Neuropsychological Test Battery (Verbal fluency). RESULTS: Language and verbal fluency impairments were identified in patients with aSAH in the LMCA territory when compared with the control population (50 individuals). Of the 26 patients with aSAH, 11 presented aphasic characteristics preoperatively. CONCLUSION: The results of this research corroborate the literature, showing that the aSAH frame causes cognitive impairments even in the preoperative phase for aneurysm occlusion. Considering the observed aspects, the predominant aphasic syndromes characterize comprehension aphasia due to sequels in the posterior cerebral artery territory.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.005

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 head0.330
Teacher spread0.292 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCoDASSame topicNeurobiology of Language and BilingualismFrench-language works237,207