Evidence map›Paper›PMID 35912191›Full record

ArticleFrontiers in oncology2022

Short-Term Surgical Outcomes for Lobectomy Between Robot-Assisted Thoracic Surgery and Uniportal Video-Assisted Thoracoscopic Surgery.

Fan Zhang, Lin Xu, Hongda Lu, Anqun Ma, Gongchao Wang

Abstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
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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.

Fan ZhangDepartment of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Lin XuDepartment of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Hongda LuDepartment of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Anqun MaDepartment of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Gongchao WangDepartment of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To evaluate the short-term outcomes of uniportal video-assisted thoracoscopic surgery (UVATS) and Da Vinci robot-assisted thoracoscopic surgery (RATS) in lobectomy and lymph node (LN) dissection. Methods: The two groups of patients with primary non-small cell lung cancer (NSCLC; RATS group, UVATS group) were matched by the propensity score to compare LN dissection and recent clinical outcomes. The results were analyzed by univariate analysis. From November 2020 to November 2021, 412 NSCLC patients (54 RATS and 358 UVATS) from a single institution of the Provincial Hospital affiliated with Shandong First Medical University were included in the analysis. Age, sex, lung lobe, surgical resection scope, solid nodules, and core tumor ratios were matched according to different surgical methods. Results: From November 2020 to November 2021, 412 patients with NSCLC (54 RATS, 358 UVATS) from the Provincial Hospital affiliated with Shandong First Medical University were included in the analysis. According to our matching results, LN dissection was more thorough in the RATS group. Conclusion: RATS has potential advantages over UVATS in radical lung cancer surgery.

Indexed as

lung cancerlymph node dissectionRATSshort-term outcomesUVATS

Identifiers

PMID35912191
PMCPMC9326123

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Registered trials

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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.