Evidence map›Paper›PMID 42509501›Full record

ArticleSurgical endoscopy2026

Mapping the learning curve of robotic cholecystectomy: a multi‑surgeon cohort analysis.

Carlo Alberto Schena, Maria Teresa Mita, Cataldo De Palma, Gianfranco Bianco, Aurora Marotta, Giorgia Grillea, Antonella Delvecchio, Riccardo Memeo, Massimo Giuseppe Viola, Elisa Reitano and 1 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Carlo Alberto Schena *Department of General Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy.
Maria Teresa Mita *Department of General, Robotic, Oncologic Surgery, Cardinale "G. Panico" Hospital, Tricase, Lecce, Italy.
Cataldo De PalmaUnit of Hepato-Biliary and Pancreatic Surgery, Ecclesiastical Entity Regional General Hospital "F. Miulli", Acquaviva Delle Fonti, 70021, Bari, Italy.
Gianfranco BiancoDepartment of General, Robotic, Oncologic Surgery, Cardinale "G. Panico" Hospital, Tricase, Lecce, Italy.
Aurora MarottaDepartment of General Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy.
Giorgia GrilleaDepartment of General, Robotic, Oncologic Surgery, Cardinale "G. Panico" Hospital, Tricase, Lecce, Italy.
Antonella DelvecchioUnit of Hepato-Biliary and Pancreatic Surgery, Ecclesiastical Entity Regional General Hospital "F. Miulli", Acquaviva Delle Fonti, 70021, Bari, Italy.
Riccardo MemeoUnit of Hepato-Biliary and Pancreatic Surgery, Ecclesiastical Entity Regional General Hospital "F. Miulli", Acquaviva Delle Fonti, 70021, Bari, Italy.
Massimo Giuseppe ViolaDepartment of General, Robotic, Oncologic Surgery, Cardinale "G. Panico" Hospital, Tricase, Lecce, Italy.
Elisa ReitanoDivision of Digestive Surgery, University of Strasbourg, Strasbourg, France. elisa.reitano@live.it.ORCID http://orcid.org/0000-0002-4286-8866
Nicola De'AngelisDepartment of General Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRobotic cholecystectomy is increasingly adopted as an alternative to laparoscopic cholecystectomy and proposed as an entry‑level procedure in robotic training curricula, yet real‑world data on surgeon‑specific learning curves and their impact on perioperative safety remain limited. This study aimed to map the learning curve for robotic cholecystectomy across a multi-surgeon, multicenter cohort and assess associated safety outcomes.

methodsThis retrospective cohort study analyzed the first 50 consecutive robotic cholecystectomies performed independently by each of five surgeons (n = 250) between January 2023 and June 2025. For each surgeon, the learning-curve endpoint was identified as the breakpoint of a piecewise linear regression of skin-to-skin operative time against case sequence, and the Mann-Whitney U test assessed whether operative time differed significantly between the early and late phases. A linear mixed-effects model, with surgeon as random intercept, identified independent predictors of operative time accounting for within-surgeon clustering.

resultsThe learning-curve endpoint ranged from 11 to 34 cases across surgeons. The breakpoint corresponded to a significant reduction in operative time for three of five surgeons (p = 0.017, p = 0.015, p < 0.001), but not for the remaining two (p = 0.853, p = 0.233). Cohort-level median operative time decreased significantly from early to late phase (66 vs 50 min; p < 0.001), while length of stay did not differ (p = 0.354). Case sequence number (- 0.72 min/case) and Nassar difficulty grade (+ 9.8 min/grade) were independent predictors of operative time (p < 0.001). No bile duct injuries occurred and severe complications occurred in 1/250 patients (0.4%).

conclusionsMost surgeons showed a statistically confirmed reduction in operative time within their first 11-24 robotic cholecystectomies, while for others no significant improvement was confirmed. Complication rates remained low, although this cohort was not adequately powered to formally demonstrate safety equivalence across the learning process. These findings support robotic cholecystectomy as a feasible early procedure within structured robotic training pathways.

Indexed as

CholecystectomyLearning CurveRobotic Surgical ProceduresAdultAgedCholecystectomy, LaparoscopicClinical CompetenceFemaleHumansMaleMiddle AgedOperative TimeRetrospective StudiesLearning curveLearning-curve methodologyProficiencyRobotic cholecystectomySurgical training

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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.