Evidence map›Paper›PMID 36793987›Full record

ArticleAnnals of cardiothoracic surgery2023

The learning curve of the robotic-assisted lobectomy-a systematic review and meta-analysis.

Ashley R Wilson-Smith, Naomi Anning, Benjamin Muston, Aditya Eranki, Michael L Williams, Christian J Wilson-Smith, Diego G Rivas, Tristan D Yan

Open access · diamondAbstract read
In one paragraph

Article in Annals of cardiothoracic surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
7.0field-weighted citation impact, top 3% of its field
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

28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 27 citations in OpenAlex.

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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

8 authors at 6 institutions in 3 countries.

Ashley R Wilson-Smith *The Chris O'Brien Lifehouse Center, Sydney, Australia.
Naomi Anning *Department of Vascular Surgery, The John Hunter Hospital, Newcastle, Australia.
Benjamin MustonThe Collaborative Research Group (CORE), Sydney, Australia.
Aditya ErankiThe St. George Hospital, Sydney, Australia.
Michael L WilliamsThe Collaborative Research Group (CORE), Sydney, Australia.
Christian J Wilson-SmithThe University of New South Wales Medical School, Sydney, Australia.
Diego G RivasDepartment of Thoracic Surgery and Minimally Invasive Thoracic Surgery Unit (UCTMI), Coruña University Hospital, A Coruña, Spain.
Tristan D YanThe Chris O'Brien Lifehouse Center, Sydney, Australia.
Collaborative Research Group · AUJohn Hunter Hospital · AUDunedin Public Hospital · NZSt George Hospital · AUUniversidade da Coruña · ESUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Early studies have illustrated the robotic lobectomy to be safe, oncologically effective, and economically feasible as a therapeutic modality in the treatment of thoracic malignancies. The 'challenging' learning curve seemingly associated with the robotic approach, however, continues to be an often-cited factor to its ongoing uptake, with the overwhelming volume of these surgeries being performed in centers of excellence where extensive experience with minimal access surgery is the norm. An exact quantification of this learning curve challenge, however, has not been made, begging the question of whether this is an outdated assumption, versus fact. This systematic review and meta-analysis sort to clarify the learning curve for robotic-assisted lobectomy based on the existing literature. Methods: An electronic search of four databases was performed to identify relevant studies outlining the learning curve of robotic lobectomy. The primary endpoint was a clear definition of operator learning (e.g., cumulative sum chart, linear regression, outcome-specific analysis, etc.) which could be subsequently aggregated or reported. Secondary endpoints of interest included post-operative outcomes and complication rates. A meta-analysis using a random effects model of proportions or means was applied, as appropriate. Results: The search strategy identified twenty-two studies relevant for inclusion. A total of 3,246 patients (30% male) receiving robotic-assisted thoracic surgery (RATS) were identified. The mean age of the cohort was 65.3±5.0 years. Mean operative, console and dock time was 190.5±53.8, 125.8±33.9 and 10.2±4.0 minutes, respectively. Length of hospital stay was 6.1±4.6 days. Technical proficiency with the robotic-assisted lobectomy was achieved at a mean of 25.3±12.6 cases. Conclusions: The robotic-assisted lobectomy has been illustrated to have a reasonable learning curve profile based on the existing literature. Current evidence on the oncologic efficacy and purported benefits of the robotic approach will be bolstered by the results of upcoming randomized trials, which will be critical in supporting RATS uptake.

Indexed as

learning curverobotic lobectomyRobotic thoracic surgery

Identifiers

PMID36793987
PMCPMC9922770
OpenAlexW4316463799

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.