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Traumatic Brain injury and Cannabis Use : a Primer For Clinicians

2017· other· en· W6946125518 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioBoard Library Catalog (Open Research Library) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGestational periodTSG101NucleofectionArticular cartilage damageProteogenomicsFusible alloyHyporeflexia

Abstract

fetched live from OpenAlex

Our clinical experience at a specialized brain injury clinic suggests that numerous patients with traumatic brain injury (TBI) are using cannabis to alleviate their symptoms. While this patient population often inquires about the evidence of using cannabis post head injury for the neurosensory, neurocognitive and neuropsychiatric sequelae, most health professionals have little to no knowledge of this evidence. Given the recent legalization of recreational cannabis in Canada, questions and guidance related to cannabis use following a traumatic brain injury are likely to become more common. This article reviews the evidence for cannabis use in psychiatric disorders with or without traumatic brain injury. Overall, we found that the evidence for the use of cannabis amongst TBI patients is sparse, and that patients tend to have little knowledge of the proven benefits and diverse effects of cannabis use. We feel this paper can serve as a stepping stone for future studies that explore the impact of cannabis use in a TBI population and can guide clinicians in advising their patients.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.072
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0280.010
Science and technology studies0.0010.003
Scholarly communication0.0160.029
Open science0.0110.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.004

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 head0.198
GPT teacher head0.430
Teacher spread0.232 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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