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Record W4388268207 · doi:10.3138/jmvfh-2022-0084

Psychedelic and nutraceutical interventions as therapeutic strategies for military-related mild traumatic brain injuries

2023· article· en· W4388268207 on OpenAlexaffvenue
Amy C. Reichelt, Eric Vermetten, Benjamin T. Dunkley

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsHospital for Sick Children
FundersUniversiteit Leiden
KeywordsConcussionMedicineTraumatic brain injuryPsychological interventionIntervention (counseling)AthletesDiseaseNutraceuticalPhysical medicine and rehabilitationPsychiatryInjury preventionPoison controlPhysical therapyInternal medicinePathologyMedical emergency

Abstract

fetched live from OpenAlex

LAY SUMMARY Concussion is a type of acquired brain injury that is common in the military, as well as among civilians and contact sport athletes, and is defined by a transient impairment in mental function. Nevertheless, concussion presents a considerable health burden, and a small minority of people suffer from continued impairment. Repetitive sub-concussive head injury is a known risk factor for neurodegenerative disease, including dementias. Concussions are difficult to treat because symptom profiles vary, but psychedelic therapies may help address some of the neurological issues that occur after brain injury. Classic psychedelics show promise as an emerging pharmacological intervention because they appear to help the brain to rewire, and they have anti-inflammatory effects. Nutraceutical interventions are widely available, cost-effective, and well tolerated, and they could also support recovery when combined with psychedelic compounds. Here, studies presenting classical psychedelics and nutraceuticals that may be combined with psychedelics as therapeutic strategies for the treatment of concussions and persistent symptoms are discussed.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.179
GPT teacher head0.464
Teacher spread0.285 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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