Evidence map›Paper›PMID 39821891›Full record

ArticleAdvances in health sciences education : theory and practice2025

Ambiguity in robotic surgical instruction: lessons from remote and in-person simulation.

Riley Brian, Laura Sterponi, Alyssa Murillo, Daniel Oh, Hueylan Chern, Elliott Silverman, Patricia O'Sullivan

Abstract read
In one paragraph

Article in Advances in health sciences education : theory and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Riley BrianDepartment of Surgery, University of California San Francisco, 513 Parnassus Avenue S-321, San Francisco, CA, 94143, USA. riley.brian@ucsf.edu.ORCID 0000-0002-7462-3214
Laura SterponiBerkeley School of Education, University of California Berkeley, Berkeley, CA, USA.
Alyssa MurilloDepartment of Surgery, University of California San Francisco, 513 Parnassus Avenue S-321, San Francisco, CA, 94143, USA.
Daniel OhDepartment of Surgery, University of Southern California, Los Angeles, CA, USA.
Hueylan ChernDepartment of Surgery, University of California San Francisco, 513 Parnassus Avenue S-321, San Francisco, CA, 94143, USA.
Elliott SilvermanIntuitive Surgical, Sunnyvale, CA, USA.
Patricia O'SullivanDepartment of Surgery, University of California San Francisco, 513 Parnassus Avenue S-321, San Francisco, CA, 94143, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rise of robotic surgery has been accompanied by numerous educational challenges as surgeons and trainees learn skills unique to the robotic platform. Remote instruction is a solution to provide surgeons ongoing education when in-person teaching is not feasible. However, surgical instruction faces challenges from unclear communication. We aimed to describe, examine, and compare ambiguities in remote and in-person robotic instruction. We designed a simulation scenario in which a standardized learner performed tasks in robotic surgery while making pre-scripted errors. Instructors provided remote or in-person instruction to the standardized learner. We applied tools from discourse analysis to transcribe sessions, identify instructional instances, classify ambiguities, and select passages for further review. We used tests of proportions to compare ambiguities between the settings. We conducted four simulation sessions, including two remote and two in-person sessions, and identified 206 instructional instances. Within these, we found 964 occurrences of three common semantic ambiguities, or ambiguities arising from words alone. Instructors used visual tools - thus employing multimodality - to clarify semantic ambiguities in 32% of instructional instances. We identified a similar degree of referential ambiguity, or ambiguity for which context from multimodality did not provide clarifying information, during remote (60%) and in-person (48%) instructional instances (p = 0.08). We described, examined, and compared ambiguities in remote and in-person instruction for simulated robotic surgery. Based on the high prevalence of ambiguity in both settings, we recommend that robotic instructors decrease referential ambiguity. To do so, instructors can reduce semantic ambiguity, harness multimodality, or both.

Indexed as

Education, DistanceRobotic Surgical ProceduresSimulation TrainingClinical CompetenceHumansAmbiguityCommunicationDiscourse analysisRobotic surgerySurgical instruction

Identifiers

PMID39821891
PMCPMC12572004

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.