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Record W4210442524 · doi:10.34172/aim.2022.08

Does Opium Consumption Have Shared Impact on Atherosclerotic Cardiovascular Disease and Cancer?

2022· review· en· W4210442524 on OpenAlexaff
Farzad Masoudkabir, Reza Malekzadeh, Negin Yavari, Kazem Zendehdel, Arya Mani, Ali Vasheghani‐Farahani, Andrew Ignaszewski, Mustafa Toma, Pegah Roayaei, Karam Turk-Adawi, Nizal Sarrafzadegan

Bibliographic record

VenueArchives of Iranian Medicine · 2022
Typereview
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsUniversity of British Columbia
FundersNational Heart, Lung, and Blood Institute
KeywordsOpiumMedicineDiseaseDiabetes mellitusAddictionDyslipidemiaEnvironmental healthCancerInternal medicinePsychiatryEndocrinology

Abstract

fetched live from OpenAlex

Although atherosclerotic cardiovascular disease (ASCVD) and cancer are seemingly different types of disease, they have multiple shared underlying mechanisms and lifestyle-related risk factors like smoking, unhealthy diet, excessive alcohol consumption, and inadequate physical activity. Opium abuse is prevalent in developing countries, especially the Middle East region and many Asian countries. Besides recreational purposes, many people use opium based on a traditional belief that opium consumption may confer protection against heart attack and improve the control of the risk factors of ASCVD such as diabetes mellitus, hypertension, and dyslipidemia. However, scientific reports indicate an increased risk of ASCVD and poor control of ASCVD risk factors among opium abusers compared with nonusers. Moreover, there is accumulating evidence that opium consumption exerts potential carcinogenic effects and increases the risk of developing various types of cancer. We conducted a review of the literature to review the current evidence on the relationship between opium consumption and ASCVD as well as various kinds of cancer. In addition, we will discuss the potential shared pathophysiologic mechanisms underlying the association between opium abuse and both ASCVD and cancer.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.101
GPT teacher head0.345
Teacher spread0.244 · 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 designOther design
Domainnot available
GenreReview

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

Citations10
Published2022
Admission routes1
Has abstractyes

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