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Record W2344775595

Physical reality simulation for training of laparoscopists in the 21st century. A multispecialty, multi-institutional study.

2005· article· en· W2344775595 on OpenAlexaboutno aff
Alessandro Fichera, Vivek N. Prachand, Sari Kives, Ronald L. Levine, Harrith M. Hasson

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual realitySpecialtyMedicineTest (biology)Laparoscopic surgerySimulation trainingObstetrics and gynaecologyMedical physicsSimulationPhysical therapyLaparoscopyComputer scienceGeneral surgeryArtificial intelligenceFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Simulation is the most effective and safe way to train laparoscopic surgeons in an era of limited work hours, lack of funding, and increasing malpractice costs. However, the costs associated with the use of virtual reality simulators are significant, and although very technically sophisticated they still lack tactile feedback. We are proposing a physical reality simulator, the LTS 2000, as a reliable and effective alternative to virtual reality. This study was carried out to establish how reliably the simulator was able to differentiate between different levels of laparoscopic experience and to analyze the detection of skills improvement after simulation and clinical training. METHODS: This study was carried out, between July 2002 and August 2003, in the departments of Surgery and Obstetrics and Gynecology at 2 separate institutions. We enrolled 40 individuals in the study who had experience ranging from postgraduate year-1 to full-time faculty level. Five postgraduate year-3 residents were subsequently retested after rotating on clinical services, performing advanced laparoscopic procedures to assess whether the simulator was sensitive enough to detect improvements in laparoscopic skills at the intermediate level. Six tasks were included in the test, and they were scored for speed and precision with the McGill system. Two scores were obtained: a coordination score and a suturing score combined in a total score. Other variables analyzed were handedness, specialty, number of laparoscopic procedures performed, and hours spent on the simulator. RESULTS: Forty-five tests were performed. The number of subjects in each group based on level of experience was equally distributed. No difference occurred in scores between institutions, specialty, and right- or left-handed surgeons. A significant increase occurred in the coordination score and suturing score combined in the total score with increasing experience (P < 0.05) at each level. Furthermore, the simulator was sensitive enough to detect a significant difference in all 3 scores between subjects who had practiced with the simulator before being tested (P < 0.05). The scores of the 5 postgraduate year-3 participants doubled when tested, without reaching statistical significance due to the small sample size. CONCLUSIONS: Our study shows that the LTS 2000 reliably and reproducibly detects different levels of laparoscopic expertise and progression of the learning curve. LTS 2000 as a model of physical reality simulation should be considered a reliable alternative to virtual reality simulation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.138
GPT teacher head0.371
Teacher spread0.233 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations30
Published2005
Admission routes1
Has abstractyes

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