SynthesisSurgical endoscopy2026
Objective technical skills assessment in laparoscopic cholecystectomy: a systematic review of manual, kinematic, and AI-based tools.
Synthesis 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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTechnical errors during surgery are a major contributor to preventable adverse outcomes, driving demand for objective assessment tools. Laparoscopic cholecystectomy (LC) is a commonly performed procedure with a clearly defined set of procedure-related complications, making it an ideal candidate for targeted assessment.
objectiveTo evaluate manual, kinematic, and AI-based technical skills assessment tools for LC and compare validity evidence and methodological quality.
methodsA PRISMA 2020-compliant systematic review (PROSPERO CRD420251125937) searched MEDLINE, Embase, Web of Science, and Cochrane Library from inception to 12 August 2025. Studies evaluating objective LC skill assessment tools were included. Two reviewers independently performed screening. Data extraction and study appraisal were performed with independent verification by a second reviewer. Validity was assessed using Messick's framework, methodological quality using MERSQI, and risk of bias using COSMIN for manual and kinematic studies and QUADAS-2 for AI studies. Results were synthesised narratively.
resultsSixty studies were included (41 manual, 6 kinematic, 13 AI). Most were single centre and retrospective. Manual tools demonstrated the largest body of validity evidence across independent cohorts, though reliability was variable and outcome associations were lacking. Kinematic systems quantified motion but had limited validation. AI systems showed strong internal performance and early real-time use, particularly for critical view of safety detection, but lacked external validation and clinical impact evidence. LIMITATIONS: Heterogeneity precluded meta-analysis and limited assessment of reporting bias and certainty.
conclusionsNo tool demonstrated sufficient validity to represent a gold standard. Manual tools are most mature but lack scalability, while AI systems show promise but require robust validation and evidence of clinical impact.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.