Association of Shanghai air pollution with postoperative infection in adolescent orthopedic patients: a study using a deep learning-based evolutionary model.

Yufeng Wang, Yang Yuan, Yang Liu, Haoqi Cai

Abstract read

In one paragraph

Article in Frontiers in artificial intelligence, 2025. 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

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

4 authors.

Yufeng Wang *Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Yang Yuan *Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Yang LiuShanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Haoqi CaiShanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Surgical site infections (SSI) represent severe complications in adolescent orthopedic surgery. Shanghai's complex air pollution profile creates a critical context to investigate multi-pollutant impacts on SSI risk in this vulnerable population. Methods: We analyzed 32,261 adolescent SSI cases from Shanghai (2019-2024) alongside high-resolution pollution/meteorological data. An evolutionary deep learning model (CNN-BiGRU-Attention optimized by Improved StarFish Algorithm) and generalized additive models (GAMs) assessed lagged effects, age/gender stratification, and concentration-response relationships. Results: NO Conclusion: Multi-pollutant exposure modulates SSI risk in adolescents, with NO

Indexed as

adolescent orthopedicsair pollutiondeep learningsurgical site infectiontime-series analysis

Identifiers

PMID41459575

DOI10.3389/frai.2025.1692207

PMCPMC12738831

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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.