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Record W7132890871

Investigating the Endocannabinoid System in Major Depressive Episodes: Imaging with the Radiotracer [11C]CURB

2022· dissertation· W7132890871 on OpenAlexfundno aff
Dorsa Rafiei

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsFatty acid amide hydrolaseApathyEndocannabinoid systemDepression (economics)Major depressive episodeNeuroimaging
DOInot available

Abstract

fetched live from OpenAlex

Purpose. To determine whether brain fatty acid amide hydrolase (FAAH) is elevated in vivo in patients with major depressive episodes (MDE) and if FAAH is related to depressive severity.Methods. Medication-free MDE and healthy control (HC) participants were recruited. All participants were administered the Structured Clinical Interview for DSM-5 and Hamilton Depression Rating Scale. FAAH was measured using [11C]CURB positron emission tomography. Results. Seven MDE and 10 age- and sex-matched HC participants completed the study. Data showed no significant group differences in FAAH, and no correlation between FAAH and depressive severity in MDE. FAAH in the medial prefrontal cortex, amygdala, ventral striatum, and substantia nigra were positively correlated with apathy in MDE. It should be noted that this study was underpowered. Conclusion. This study is the first to investigate FAAH in MDE in vivo. Despite no significant group differences in FAAH levels, FAAH may be related to apathy in MDE.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.013
GPT teacher head0.321
Teacher spread0.309 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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