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Record W1631875608 · doi:10.3233/nre-2005-20405

Treatment of agitation following traumatic brain injury: A review of the literature

2005· review· en· W1631875608 on OpenAlexaff
Matthew N. Levy, Andrea Berson, Theresa Cook, Natasha Bollegala, Eva Seto, Shannon Tursanski, Jennifer Kim, Sanjeev Sockalingam, Anshu Rajput, Nupura Krishnadev, Chris Feng, Shree Bhalerao

Bibliographic record

VenueNeurorehabilitation · 2005
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsTraumatic brain injuryMedicineAnesthesiaPsychiatry

Abstract

fetched live from OpenAlex

Agitation, restlessness, and aggression are frequent neurobehavioural sequelae in the early stages of recovery from traumatic brain injury (TBI). These behavioural symptoms disrupt patient care and impede rehabilitation efforts. We review the current literature (1985 onwards) examining the pharmacological management of post-TBI agitation in both acute and post-acute conditions. This article will assess the evidence for the use of selected alkylphenols, benzodiazepines, estrogens, antiandrogens, neuroleptics/antipsychotics, antidepressants, anti-Parkinsonian agents, antipsychotics, anticonvulsants, lithium carbonate, buspirone, beta-blockers, and psychostimulants in agitated TBI survivors. Review of the literature suggests that there is limited evidence to accurately guide clinicians in the management of this patient population.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.738
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
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 head0.111
GPT teacher head0.448
Teacher spread0.337 · 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 designOther design
Domainnot available
GenreReview

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

Citations84
Published2005
Admission routes1
Has abstractyes

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