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Record W2333055802 · doi:10.4293/jsls.2014.00142

Comparing Technical Dexterity of Sleep-Deprived Versus Intoxicated Surgeons

2014· article· en· W2333055802 on OpenAlexaff
Fariba Mohtashami, Allison Thiele, Erwin Karreman, John Thiel

Bibliographic record

VenueJSLS Journal of the Society of Laparoscopic & Robotic Surgeons · 2014
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsRegina Qu'Appelle Health RegionUniversity of Saskatchewan
Fundersnot available
KeywordsSleep (system call)MedicinePsychologyPhysical medicine and rehabilitationComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: The evidence on the effect of sleep deprivation on the cognitive and motor skills of physicians in training is sparse and conflicting, and the evidence is nonexistent on surgeons in practice. Work-hour limitations based on these data have contributed to challenges in the quality of surgical education under the apprentice model, and as a result there is an increasing focus on competency-based education. Whereas the effects of alcohol intoxication on psychometric performance are well studied in many professions, the effects on performance in surgery are not well documented. To study the effects of sleep deprivation on the surgical performance of surgeons, we compared simulated the laparoscopic skills of staff gynecologists "under 2 conditions": sleep deprivation and ethanol intoxication. We hypothesized that the performance of unconsciously competent surgeons does not deteriorate postcall as it does under the influence of alcohol. METHODS: Nine experienced staff gynecologists performed 3 laparoscopic tasks in increasing order of difficulty (cup drop, rope passing, pegboard exchange) on a box trainer while sleep deprived (<3 hours in 24 hours) and subsequently when legally intoxicated (>0.08 mg/mL blood alcohol concentration). Three expert laparoscopic surgeons scored the anonymous clips online using Global Objective Assessment of Laparoscopic Skills criteria: depth perception, bimanual dexterity, and efficiency. Data were analyzed by a mixed-design analysis of variance. RESULTS: There were large differences in mean performance between the tasks. With increasing task difficulty, mean scores became significantly (P < .05) poorer. For the easy tasks, the scores for sleep-deprived and intoxicated participants were similar for all variables except time. Surprisingly, participants took less time to complete the easy tasks when intoxicated. However, the most difficult task took less time but was performed significantly worse compared with being sleep deprived. Notably, the evaluators did not recognize a lack of competence for the easier tasks when intoxicated; incompetence surfaced only in the most difficult task. CONCLUSIONS: Being intoxicated hinders the performance of more difficult simulated laparoscopic tasks than being sleep deprived, yet surgeons were faster and performed better on simple tasks when intoxicated.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.302
Teacher spread0.261 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

Explore more

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