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Record W135628093

The role of injury severity in neurobehavioral outcome 3 months after traumatic brain injury.

2002· article· en· W135628093 on OpenAlexaff
Mark Rapoport, Stephen R. McCauley, Harvey S. Levin, James Song, Anthony Feinstein

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsTraumatic brain injuryIrritabilityGlasgow Outcome ScalePsychologyAnxietyGlasgow Coma ScaleDepression (economics)CognitionPoison controlCohortClinical psychologyPsychiatryPhysical therapyMedicineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE/BACKGROUND: To assess neurobehavioral outcome using the Neurobehavioral Rating Scale-Revised (NRS-R), an instrument with established specificity and validity in Traumatic Brain Injury (TBI) in a sample including the full spectrum of TBI severity 3 months after injury. METHOD: A cohort group of 102 subjects with mild TBI, 41 with moderate TBI, and 139 with severe TBI, from multiple academic trauma centers, were assessed using the NRS-R and the Glasgow Outcome Scale. RESULTS: Principal components analysis of the NRS-R resulted in a 3-factor model: (1) Cognitive, (2) Emotional, and (3) Hyperarousal. At 3 months, subjects with severe TBI show greater difficulties in cognitive and hyperarousal, but not emotional domains, than those with mild to moderate TBI. More than one third of subjects in all injury severity groups showed evidence of anxiety, depression, irritability, mental fatigability, and memory dysfunction. Scores on the NRS-R were related to outcome on the Glasgow Outcome Scale. CONCLUSIONS: Three months after injury, subjects with severe TBI have more dysfunction in cognitive and behavioral (but not emotional) domains than those with mild-to-moderate TBI. The NRS-R is a useful tool for assessing the full spectrum of neurobehavioral dysfunction at all ranges of TBI severity.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
Research integrity0.0000.001
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 head0.073
GPT teacher head0.321
Teacher spread0.248 · 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

Citations76
Published2002
Admission routes1
Has abstractyes

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