MétaCan
Menu
Back to cohort
Record W4390395259 · doi:10.37349/emed.2023.00201

Risk factors for the development of lung cancer around the world: a review

2023· review· en· W4390395259 on OpenAlexaff
Hervé Agonsanou, Ricardo G. Figueiredo, Maurice Bergeron

Bibliographic record

VenueExploration of Medicine · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLung cancerMedicineCancerIntensive care medicineDiseaseDiabetes mellitusEnvironmental healthLungInternal medicine

Abstract

fetched live from OpenAlex

According to recent data reported, it is noted that lung cancer is the leading cause of cancer death internationally followed by cardiovascular diseases and diabetes. This disease is observed in both women and men and is related to lifestyle habits. Several causes are reported to be at the origin of lung cancer, especially smoking. It is important to note that the majority of lung cancers develop in the bronchi, that is to say at the level of the upper airways which lead to the lungs, which does not however make it possible to rule out the risk factors that come under environmental pollution since man breathes the air quality of the environment every day for his breathing. This review of the literature has made it possible to draw up a state of knowledge in order to understand the risk factors that increase lung cancer. More specifically, this work will make it possible to raise awareness in the field of the fight against cancer, in particular lung 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 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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.326
GPT teacher head0.479
Teacher spread0.154 · 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
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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueExploration of MedicineSame topicAir Quality and Health ImpactsFrench-language works237,207