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Record W1520713631 · doi:10.26443/mjm.v11i1.455

The significance of nanoparticles in particle- induced pulmonary fibrosis

2020· article· en· W1520713631 on OpenAlexvenueno aff
James D. Byrne, John A. Baugh

Bibliographic record

VenueMcGill Journal of Medicine · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersDirectorate for Mathematical and Physical Sciences
KeywordsPulmonary fibrosisPneumoconiosisMedicineFibrosisNanoparticleLungParticle depositionDeposition (geology)Particle (ecology)Intratracheal instillationLung fibrosisPathologyImmunologyRange (aeronautics)Internal medicineNanotechnologyMaterials scienceBiologyComposite material

Abstract

fetched live from OpenAlex

Exposure to airborne nanoparticles contributes to many chronic pulmonary diseases. Nanoparticles, classified as anthropogenic and natural particles, and fibers of diameters less than 100 nm, have unrestricted access to most areas of the lung due to their size. Size relates to the deposition efficiency of the particle, with particles in the nano-range having the highest efficiencies. The deposition of nanoparticles in the lung can lead to chronic inflammation, epithelial injury, and further to pulmonary fibrosis. Cases of particle-induced pulmonary fibrosis, namely pneumoconiosis, are mostly occupationally influenced, and continue to be documented around the world. The tremendous growth of nanotechnology, however, has spurred fears of increased rates of pulmonary diseases, especially fibrosis. The severity of toxicological consequences warrants further examination of the effects of nanoparticles in humans, possible treatments and increased regulatory measures.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.092
GPT teacher head0.318
Teacher spread0.226 · 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 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

Citations147
Published2020
Admission routes1
Has abstractyes

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