Evidence map›Paper›PMID 37922066›Full record

ArticleJournal of robotic surgery2023

Learning curve for robotic rectal cancer resection at a community-based teaching institution.

Kristen Coleman, Angela N Fellner, Hamza Guend

Abstract read
In one paragraph

Article in Journal of robotic surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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

7 citing papers in PubMed.

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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

3 authors.

Kristen ColemanDepartment of Surgery, TriHealth, 375 Dixmyth Ave, Cincinnati, OH, 45220, USA. kristen_coleman@trihealth.com.ORCID http://orcid.org/0000-0002-7435-6144
Angela N FellnerTriHealth Hatton Research Institute, Cincinnati, OH, USA.ORCID http://orcid.org/0000-0002-7799-7430
Hamza GuendTriHealth Surgical Institute, Cincinnati, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The surgical management of rectal cancer is shifting toward more widespread use of robotics across a spectrum of medical centers. There is evidence that the oncologic outcomes are equivalent to laparoscopic resections, and the post-operative outcomes may be improved. This study aims to evaluate the learning curve of robotic rectal cancer resections at a community-based teaching institution and evaluate clinical and oncologic outcomes. A retrospective review of consecutive robotic rectal cancer resections by a single surgeon was performed for a five-year period. The cumulative sum (CUSUM) for total operative time was calculated and plotted to establish a learning curve. The oncologic and post-operative outcomes for each phase were analyzed and compared. The CUSUM learning curve yielded two phases, the learning phase (cases 1-79) and the proficiency phase (cases 80-130). The median operative time was significantly lower in the proficiency phase. The type of neoadjuvant therapy used between the two groups was statistically different, with chemoradiation being the primary regimen in the learning phase and total neoadjuvant therapy being more common in the proficiency phase. Otherwise, oncologic and overall post-operative outcomes were not significantly different between the groups. Robotic rectal resections can be done in a community-based hospital system by trained surgeons with outcomes that are favorable and similar to larger institutions.

Indexed as

LaparoscopyRectal NeoplasmsRoboticsRobotic Surgical ProceduresHumansLearning CurveOperative TimeRectumRetrospective StudiesLearning curveRectal cancerRobotic surgery

Identifiers

PMID37922066
PMCPMC10678792

What OpenQuestion holds

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LicenceCC BY
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.