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Record W4392190375 · doi:10.36401/iddb-23-5

Betel Nuts, Health Policies, and Adolescent Health

2023· article· en· W4392190375 on OpenAlexaff
Jasper Hoi Chun Luong, Zisis Kozlakidis, Io Hong Cheong, Hui Wang

Bibliographic record

VenueInnovations in Digital Health Diagnostics and Biomarkers · 2023
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsAdolescent healthBetelEnvironmental healthMedicineTraditional medicineNutNursingEngineering

Abstract

fetched live from OpenAlex

ABSTRACT Areca nut and betel quid (AN/BQ) products are largely scrutinized by the scientific community because of their toxicological and carcinogenic properties. However, at the same time there exists an ever-growing user base in low- and medium-income countries, whose users are responding to innovative products preparation processes and are initiated to AN/BQ products by their parents and family at a young age. This report compiles current cessation policies, implemented interventions, and comments on their corresponding effectiveness and/or potential effectiveness. The report also highlights the need for further research from both an adolescent health and a Chinese perspective, as data regarding the region with the second largest user group after India are largely unknown or unavailable for scientific review. Ultimately, recent studies involving analytical methods to observe how different cultivation environments, and/or processing methods change the chemical composition of the AN/BQ product have also presented a potential insight in better understanding and eventually regulating AN/BQ across all population groups, including adolescents. The rise of digital solutions may also encourage development of applications to track consumption and usage and distribution of AN/BQ products for policy makers to design targeted campaigns.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.043
GPT teacher head0.365
Teacher spread0.322 · 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 routes1
Has abstractyes

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