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Record W2953985018 · doi:10.1186/s42238-019-0004-y

Molecular neuroscience at its “high”: bibliometric analysis of the most cited papers on endocannabinoid system, cannabis and cannabinoids

2019· article· en· W2953985018 on OpenAlexfundno aff
Andy Wai Kan Yeung, Nikolay Tzvetkov, Nicolas Arkells, Luigi Milella, Adrian Stankiewicz, Łukasz Huminiecki, Olaf K. Horbańczuk, Atanas G. Atanasov

Bibliographic record

VenueJournal of Cannabis Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPharmacological Receptor Mechanisms and Effects
Canadian institutionsnot available
FundersInstitute of GeneticsSzkola Glówna Gospodarstwa Wiejskiego w WarszawieKrajowy Naukowy Osrodek WiodacyUniversità degli Studi della BasilicataBulgarian Academy of SciencesUniversität WienRheinische Friedrich-Wilhelms-Universität BonnUniversity of Hong KongNational Research Centre
KeywordsCitationCannabisLibrary scienceBibliometricsWeb of scienceCitation analysisMedicinePsychologyPsychiatryInternal medicineComputer scienceMeta-analysis

Abstract

fetched live from OpenAlex

BACKGROUND: Cannabis, cannabinoids and endocannabinoids are heavily investigated topics with many articles published every year. We aimed to identify the 100 most cited manuscripts among the vast literature and analyze their contents. METHODS: Web of Science (WoS) Core Collection was searched to identify the 100 most cited relevant manuscripts, which were analyzed with reference to (1) authorship, (2) institution, (3) country, (4) document type, (5) journal, (6) publication year, (7) WoS category, and (8) citation count. Semantic content and citation data of the manuscripts were analyzed with VOSviewer. RESULTS: The most cited manuscripts were published between 1986 and 2016, with the majority being published in the 2000s (n = 51). The number of citations for the top 100 articles ranged from 469 to 3651, with a median citation count of 635.5. The most prolific authors were Vincenzo Di Marzo (n = 11) and Daniele Piomelli (n = 11). The major contributing countries were USA (n = 49), Italy (n = 22), UK (n = 19), and France (n = 11). The most prolific institutions were University of California (n = 14), National Research Council of Italy (n = 12) and National Institutes of Health USA (n = 12). The manuscripts consisted of original articles (n = 75), reviews (n = 24) and a note (n = 1). The most dominant journal was Nature (n = 15). The major WoS categories associated were Multidisciplinary sciences (n = 31), Neurosciences (n = 20), Pharmacology / Pharmacy (n = 16), and General / Internal Medicine (n = 11). CONCLUSIONS: The top-ranked manuscripts among the 100 were concerning analgesia, weight loss, long-term potentiation, depolarization-induced suppression of inhibition, opiates and other topics. Cannabinoid type 1 (CB1) receptor was studied by more of the top 100 papers in comparison to cannabinoid type 2 (CB2) receptor. The most frequently mentioned chemicals in these publications were 2-arachidonoylglycerol, tetrahydrocannabinol, and anandamide. Together, these manuscripts comprise the most highly cited publications in the topic, literally the molecular neuroscience at its "high".

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0110.040
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.324
Teacher spread0.305 · 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.

Study designBench or experimental
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

Citations14
Published2019
Admission routes1
Has abstractyes

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