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Record W2069566728 · doi:10.1017/s135561770606084x

Impulsivity, reward sensitivity, and decision-making in subarachnoid hemorrhage survivors

2006· article· en· W2069566728 on OpenAlexfundno aff
C. H. Salmond, Elise E. DeVito, Luke Clark, David Menon, Doris A. Chatfield, John D. Pickard, Peter J. Kirkpatrick, Barbara J. Sahakian

Bibliographic record

VenueJournal of the International Neuropsychological Society · 2006
Typearticle
Languageen
FieldPsychology
TopicCognitive Functions and Memory
Canadian institutionsnot available
FundersMedical Research CouncilAtlantic Canada Opportunities Agency
KeywordsImpulsivitySubarachnoid hemorrhageApathyPsychosocialPsychologyMedicineClinical psychologyPsychiatryCognitionAnesthesia

Abstract

fetched live from OpenAlex

Subarachnoid hemorrhage (SAH) survivors often report psychosocial and emotional changes, including a diminished capacity for decision making. However, systematic investigations into the nature of the changes have been limited to those patients surviving SAH secondary to aneurysms of the anterior communicating artery. This study aimed to explore the nature of decision making in survivors of SAH secondary to aneurysms of the middle cerebral or posterior communicating artery using a series of computerized tasks. Twenty SAH survivors and 20 matched controls completed a battery of computerized decision-making tasks. These included tasks examining an individual's ability to make probabilistic choices and risk-taking behavior, as well as tasks examining aspects of impulsivity. The results revealed two key patterns of abnormal decision-making behavior in the SAH survivors: altered sensitivity to both reward and punishment, and impulsive responding. These complex deficits may contribute to difficulties in daily living resulting from apathy, poor judgment, or inhibition in SAH survivors.

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)0.004
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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 integrity0.0000.000
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 head0.014
GPT teacher head0.300
Teacher spread0.286 · 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

Citations15
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the International Neuropsychological SocietySame topicCognitive Functions and MemoryFrench-language works237,207