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Record W4410901968 · doi:10.1186/s12909-025-07203-w

A systematic review of robotic colorectal surgery programs worldwide and a comprehensive description of local robotic training programme

2025· review· en· W4410901968 on OpenAlexaboutno aff
Valentin Butnari, Harpreet Kaur Sekhon Inderjit Singh, Eshtar Hamid, Shady Gaafar Hosny, Sandeep Kaul, Joseph Huang, Richard Boulton, N Rajendran

Bibliographic record

VenueBMC Medical Education · 2025
Typereview
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMedical educationMedicineMEDLINEConceptualizationObservational studyEducational measurementMedical physicsComputer sciencePsychologyArtificial intelligencePedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Robotic-assisted colorectal surgery (RACS) is gaining widespread adoption, with a growing number of procedures performed globally. These have been performed mostly by consultants, many of whom have gained sufficient proficiency to begin to educate their trainees. RACS offers a range of benefits to the surgeon and patient, yet safe and effective utilisation hinges on well-structured training programs for colorectal trainees within their general surgery residency. This systematic review aimed to evaluate the structure currently employed worldwide in RACS training programs for colorectal surgery trainees. In addition it delineates the conceptualization and implementation of a locally developed RACS program tailored to senior colorectal trainees and fellows at our Trust. METHODS: A comprehensive search of Ovid Medline and Embase databases (January 2010- March 2024) following PRISMA guidelines identified six studies reporting on RACS training curricula. Critical analysis of programme structure and curricula tools utilised was performed. Articles involving training of consultants were excluded. The quality and bias score of each study were assessed using the Newcastle Ottawa Score for observational studies. RESULTS: Six out of 77 studies were selected as suitable for analysis describing RACS training using Da Vinci platform. All apart from one programme described a phased or parallel robotic curriculum with four studies incorporating theoretical knowledge and laboratory or cadaveric training. Six programmes incorporated simulation, bedside assisting and console training. The use of validated objective or subjective metrics at each phase varied. Formal feedback is provided in only two of the programmes. Reflecting on above results we present our Trust training program which run over the last two years. Our program ensures clear learning goals for trainees and trainers, maintains patient safety, and is easily replicated across other UK RACS units. CONCLUSION: The establishment of a standardised curriculum for colorectal surgery training worldwide, including in the UK, is vital. Currently, there is a scarcity of validated, objective assessment methods, which must be adequately standardised to create consistent progression criteria and competency-based metrics. Standardising these methods will enable reliable and robust assessment of trainee progression and competence to create a generation of robotically competent colorectal surgeons within their standard training program timeframe. PROSPERO DATABASE REGISTRATION: No.-CRD42024530340.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.124
GPT teacher head0.376
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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