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Record W4410142669 · doi:10.1186/s12885-025-14149-1

Risk factors for pneumonia after radical gastrectomy for gastric cancer: a systematic review and meta-analysis

2025· review· en· W4410142669 on OpenAlexaboutno aff
Siyue Fan, Hongzhan Jiang, Qiuqin Xu, Jiali Shen, Huihui Lin, Liping Yang, Doudou Yu, Nengtong Zheng, Lijuan Chen

Bibliographic record

VenueBMC Cancer · 2025
Typereview
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsSurgical oncologyMedicineGastrectomyMeta-analysisCancerGeneral surgeryPneumoniaInternal medicineGastroenterologyOncology

Abstract

fetched live from OpenAlex

The objective is to systematically gather relevant research to determine and quantify the risk factors and pooled prevalence for pneumonia after a radical gastrectomy for gastric cancer. The reporting procedures of this meta-analysis conformed to the PRISMA 2020. Chinese Wan Fang data, Chinese National Knowledge Infrastructure (CNKI), Chinese Periodical Full-text Database (VIP), Embase, Scopus, CINAHL, Ovid MEDLINE, PubMed, Web of Science, and Cochrane Library from inception to January 20, 2024, were systematically searched for cohort or case–control studies that reported particular risk factors for pneumonia after radical gastrectomy for gastric cancer. The pooled prevalence of pneumonia was estimated alongside risk factor analysis. The quality was assessed using the Newcastle–Ottawa Scale after the chosen studies had been screened and the data retrieved. RevMan 5.4 and R 4.4.2 were the program used to perform the meta-analysis. Our study included data from 20,840 individuals across 27 trials. The pooled prevalence of postoperative pneumonia was 11.0% (95% CI = 8.0% ~ 15.0%). Fifteen risk factors were statistically significant, according to pooled analyses. Several factors were identified to be strong risk factors, including smoking history (OR 2.71, 95% CI = 2.09 ~ 3.50, I2 = 26%), prolonged postoperative nasogastric tube retention (OR 2.25, 95% CI = 1.36–3.72, I2 = 63%), intraoperative bleeding ≥ 200 ml (OR 2.21, 95% CI = 1.15–4.24, I2 = 79%), diabetes mellitus (OR 4.58, 95% CI = 1.84–11.38, I2 = 96%), male gender (OR 3.56, 95% CI = 1.50–8.42, I2 = 0%), total gastrectomy (OR 2.59, 95% CI = 1.83–3.66, I2 = 0%), COPD (OR 4.72, 95% CI = 3.80–5.86, I2 = 0%), impaired respiratory function (OR 2.72, 95% CI = 1.58–4.69, I2 = 92%), D2 lymphadenectomy (OR 4.14, 95% CI = 2.29–7.49, I2 = 0%), perioperative blood transfusion (OR 4.21, 95% CI = 2.51–7.06, I2 = 90%), and hypertension (OR 2.21, 95% CI = 1.29–3.79, I2 = 0%). Moderate risk factors included excessive surgery duration (OR 1.51, 95% CI = 1.25–1.83, I2 = 90%), advanced age (OR 1.91, 95% CI = 1.42–2.58, I2 = 94%), nutritional status (OR 2.62, 95% CI = 1.55–4.44, I2 = 71%), and history of pulmonary disease (OR 1.61, 95% CI = 1.17–2.21, I2 = 79%). This study identified 15 independent risk factors significantly associated with pneumonia after radical gastrectomy for gastric cancer, with a pooled prevalence of 11.0%. These findings emphasize the importance of targeted preventive strategies, including preoperative smoking cessation, nutritional interventions, blood glucose and blood pressure control, perioperative respiratory training, minimizing nasogastric tube retention time, and optimizing perioperative blood transfusion strategies. For high-risk patients, such as the elderly, those undergoing prolonged surgeries, experiencing excessive intraoperative blood loss, undergoing total gastrectomy, or receiving open surgery, close postoperative monitoring is essential. Early recognition of pneumonia signs and timely intervention can improve patient outcomes and reduce complications.

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)0.018
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.030
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.046
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.082
GPT teacher head0.387
Teacher spread0.305 · 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 designMeta-analysis
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

Citations5
Published2025
Admission routes1
Has abstractyes

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