Evidence map›Paper›PMID 42499649›Full record

ArticleFrontiers in medical technology2026

Development of a new suction instrument to reduce surgical smoke exposure in the operating room.

Weiwei Chen, Lumin Lei, Mingzi Xiao, Qiaoling Chen, Yanlan Ma

Abstract read
In one paragraph

Article in Frontiers in medical technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Weiwei ChenDepartment of Urology, Fourth Medical Center, Chinese PLA General Hospital Beijing, China.
Lumin LeiDepartment of Anesthesiology, the Fourth Medical Center, Chinese PLA General Hospital, Beijing, China.
Mingzi XiaoDepartment of Urology, Fourth Medical Center, Chinese PLA General Hospital Beijing, China.
Qiaoling ChenNursing Department, Hainan Hospital of PLA General Hospital, Sanya, Hainan Province, China.
Yanlan MaMedical Service Training Center, Chinese PLA General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To remove surgical smoke and its toxic components, we developed a novel smoke removal tube nested with the intraoperative suction tube, enabling convenient application during surgery and reducing hazards of surgical smoke exposure to medical staff. Methods: Standardized cuts with an electric knife were performed on pig dorsal tissue in an animal laboratory operating room without vertical laminar flow. The generated surgical fumes were analyzed using microcomputer laser dust meter counters for real-time monitoring of particulate matter 2.5 μm (PM2.5). Three new design suction tubes with different orifice diameters (1.5, 2.0, and 2.5 mm), each connected to local smoke evacuation, were compared with a conventional instrument without smoke evacuation. Results: The PM2.5 levels were significantly reduced with all three new tools, with smoke reduction rates of 24.20%, 66.86%, and 43.02%, respectively. Ten seconds after cutting, the 2.0-mm-diameter suction tube demonstrated lower PM2.5 concentrations compared with the other two groups, and the difference was statistically significant ( Conclusion: This study is the first to develop an "all-in-one" surgical smoke device that enables simultaneous suction of smoke and blood with one hand. Among the tested tools, the 2.0-mm-diameter instrument is the best one. These results promote the use of local smoke evacuation and may inspire other surgeons to further innovation in this field.

Indexed as

operation roomPM2.5suction tubesurgical smokeVOCs

Identifiers

PMID42499649
PMCPMC13396161

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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.