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Record W2124376555 · doi:10.1002/dta.1891

Cannabis: a self‐medication drug for weight management? The never ending story

2015· article· en· W2124376555 on OpenAlexfundno aff
Francesco Saverio Bersani, Rita Santacroce, Marialuce Coviello, Claudio Imperatori, Marta Francesconi, Roberto Vicinanza, Amedeo Minichino, Ornella Corazza

Bibliographic record

VenueDrug Testing and Analysis · 2015
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
FundersEuropean CommissionPublic Health Agency of Canada
KeywordsCannabisDrugMedicineSelf-medicationSubstance Abuse DetectionPsychiatryPharmacology

Abstract

fetched live from OpenAlex

In a society highly focused on physical appearance, people are increasingly using the so-called performance and image-enhancing drugs (PIEDs) or life-style drugs as an easy way to control weight. Preliminary data from online sources (e.g. websites, drug forums, e-newsletters) suggest an increased use of cannabis amongst the general population as a PIED due to its putative weight-loss properties. The use of cannabis and/or cannabis-related products to lose weight may represent a new substance-use trend that should be carefully monitored and adequately investigated, especially in light of the well-known adverse psychiatric and somatic effects of cannabis, its possible interaction with other medications/drugs and the unknown and potentially dangerous composition of synthetic cannabimimetics preparations.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0040.002

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 head0.023
GPT teacher head0.295
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations9
Published2015
Admission routes1
Has abstractyes

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