Evidence map›Paper›PMID 35910918›Full record

ReviewFrontiers in public health2022

Coal Miners and Lung Cancer: Can Mortality Studies Offer a Perspective on Rat Inhalation Studies of Poorly Soluble Low Toxicity Particles?

Robert J McCunney, Mei Yong

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Robert J McCunneyHarvard Medical School, Brigham and Women's Hospital, Boston, MA, United States.
Mei YongMY EpiConsulting, Duesseldorf, Germany.
Brigham and Women's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inhalation studies involving laboratory rats exposed to poorly soluble particles (PSLTs), such as carbon black and titanium dioxide, among others, have led to the development of lung cancer in conditions characterized as lung overload. Lung overload has been described as a physiological state in which pulmonary clearance is impaired, particles are not effectively removed from the lungs and chronic inflammation develops, ultimately leading to tumor growth. Since lung tumors have not occurred under similar states of lung overload in other laboratory animal species, such as mice, hamsters and guinea pigs, the relevance of the rat as a model for human risk assessment has presented regulatory challenges. It has been suggested that coal workers' pneumoconiosis may reflect a human example of apparent "lung overload" of poorly soluble particles. In turn, studies of risk of lung cancer in coal miners may offer a valuable perspective for understanding the significance of rat inhalation studies of PSLTs on humans. This report addresses whether coal can be considered a PSLT based on its composition in contrast to carbon black and titanium dioxide. We also review cohort mortality studies and case-control studies of coal workers. We conclude that coal differs substantially from carbon black and titanium dioxide in its structure and composition. Carbon black, a manufactured product, is virtually pure carbon (upwards of 98%); TiO

Indexed as

Lung NeoplasmsMinersAnimalsCoalCricetinaeDustGuinea PigsHumansMiceRatsSootCoalDustSootcarbon blackcoal minerslung cancerpoorly soluble particlesPSLTstitanium dioxide

Identifiers

PMID35910918
PMCPMC9334883
OpenAlexW4285589840

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.