Evidence map›Paper›PMID 35106738›Full record

ReviewJournal of robotic surgery2022

Factors affecting the learning curve in robotic colorectal surgery.

Shing Wai Wong, Philip Crowe

Abstract readReview
In one paragraph

Review in Journal of robotic surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 4 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

29 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Training and assessment for colorectal surgery and appendicectomy- a systematic review.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2024
    Pooled it
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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

2 authors.

Shing Wai WongDepartment of General Surgery, Prince of Wales Hospital, Sydney, NSW, Australia. sw.wong@unsw.edu.au.ORCID http://orcid.org/0000-0002-2046-9506
Philip CroweDepartment of General Surgery, Prince of Wales Hospital, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Learning related to robotic colorectal surgery can be measured by surgical process (such as time or adequacy of resection) or patient outcome (such as morbidity or quality of life). Time based metrics are the most commonly used variables to assess the learning curve because of ease of analysis. With analysis of the learning curve, there are factors which need to be considered because they may have a direct impact on operative times or may be surrogate markers of clinical effectiveness (unrelated to times). Variables which may impact on operation time include surgery case mix, hybrid technique, laparoscopic and open colorectal surgery experience, robotic surgical simulator training, technology, operating room team, and case complexity. Multidimensional analysis can address multiple indicators of surgical performance and include variables such as conversion rate, complications, oncological outcome and functional outcome. Analysis of patient outcome and/or global assessment of robotic skills may be the most reliable methods to assess the learning curve.

Indexed as

Colorectal SurgeryDigestive System Surgical ProceduresLaparoscopyRobotic Surgical ProceduresHumansLearning CurveOperative TimeQuality of LifeLearning curveMultidimensional analysisOperative timeRobotic colorectal surgery

Identifiers

PMID35106738
PMCPMC9606100

What OpenQuestion holds

Textmetadata
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.