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Record W4386608899 · doi:10.3389/fonc.2023.1257585

Effect of indocyanine green near-infrared light imaging technique guided lymph node dissection on short-term clinical efficacy of minimally invasive radical gastric cancer surgery: a meta-analysis

2023· review· en· W4386608899 on OpenAlexaboutno aff
Sen Niu, Yuan Liu, Da Li, Yufan Sheng, Ye Zhang, Zeng-Yao Li, Songyun Zhao, Tong Wang

Bibliographic record

VenueFrontiers in Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMedicineLymph nodeIndocyanine greenDissection (medical)SurgeryCochrane LibraryAbdominal surgeryContext (archaeology)RadiologyRandomized controlled trialInternal medicine

Abstract

fetched live from OpenAlex

Objective: In recent years, the utilization of indocyanine green near-infrared (ICG NIR) light imaging-guided lymph node dissection in the context of minimally invasive radical gastric cancer has emerged as a novel avenue for investigation. The objective of this study was to assess the influence of employing this technique for guiding lymph node dissection on the short-term clinical outcomes of minimally invasive radical gastric cancer surgery. Methods: The present study conducted a comprehensive search for short-term clinical outcomes, comparing the group undergoing ICG NIR light imaging-guided lymph node dissection with the control group, by thoroughly examining relevant literature from the inception to July 2023 in renowned databases such as PubMed, Embase, Web of Science, and Cochrane Library. The primary endpoints encompassed postoperative complications, including abdominal infection, abdominal bleeding, pneumonia, anastomotic fistula, and overall incidence of complications (defined as any morbidity categorized as Clavien-Dindo class I or higher within 30 days post-surgery or during hospitalization). Additionally, secondary outcome measures consisted of the time interval until the initiation of postoperative gas and food intake, as well as various other parameters, namely postoperative hospital stay, operative time, intraoperative blood loss, total number of harvested lymph nodes, and the number of harvested metastatic lymph nodes. To ensure methodological rigor, the Cochrane Collaboration Risk of Bias Tool and the Newcastle-Ottawa Scale (NOS) were employed to assess the quality of the included studies, while statistical analyses were performed using Review Manager 5.4 software and Stata, version 12.0 software. Results: A total of 19 studies including 3103 patients were ultimately included (n=1276 in the ICG group and n=1827 in the non-ICG group). In this meta-analysis, the application of ICG near-infrared light imaging in minimally invasive radical gastric cancer surgery effectively improved the occurrence of postoperative Clavien-Dindo grade II or higher complications in patients (RR=0.72, 95% CI 0.52 to 1.00) with a statistically significant P=0.05; in reducing intraoperative blood loss and shortening While reducing intraoperative blood loss and shortening postoperative hospital stay, it could ensure the thoroughness of lymph node dissection in minimally invasive radical gastric cancer surgery (MD=5.575, 95% CI 3.677-7.473) with significant effect size (Z=5.76, p<0.00001). Conclusion: The utilization of indocyanine green near-infrared light imaging technology in the context of minimally invasive radical gastric cancer surgery demonstrates notable efficacy in mitigating the occurrence of postoperative complications surpassing Clavien-Dindo grade II, while concurrently augmenting both the overall quantity of lymph node dissections and the identification of positive lymph nodes, all the while ensuring the preservation of surgical safety. Furthermore, the implementation of this technique proves particularly advantageous in the realm of robotic-assisted radical gastric cancer surgery, thus bearing significance for enhancing the short-term prognostic outcomes of patients.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.670
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0140.006
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.423
Teacher spread0.331 · 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.

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

Citations10
Published2023
Admission routes1
Has abstractyes

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