Effects of transcranial direct current stimulation on surgical skills acquisition: a systematic review
Bibliographic record
Abstract
Background: Surgical training is opportunity based, and multiple factors including exposure time, case volume and simulation training contribute to achieving competencies.We aimed to evaluate the effects of transcranial direct current stimulation (TDCS) as an adjunct to attain surgical skills faster.Methods: A registered systematic review (PROSPERO number CRD42020211985) of randomized trials (RCTs) on Biosis, Cochrane Central, EMBASE, MEDLINE, PsycINFO databases was carried out.Studies included compared active TDCS to sham stimulation in a surgical task involving trainees.Outcomes were grouped into four domains to overcome the heterogeneity of study outcomes: speed of skills acquisition; proficiency, i.e. ability to achieve a pre-determined score/level of proficiency; accuracy and error reduction; and composite outcomes involving more than one domain.Results: Four RCTs were identified involving 143 participants in total (61 sham, 82 TDCS).All studies utilized simulation training: three in laparoscopic training (peg transfer and pattern cutting), and one in neurosurgery training (tumour resection exercise).The mean age of the participants was 24.5 Æ 1.5 years, 58% (n=83) were female and 92% (n=131) were right hand dominant.Use of TDCS was associated with improved speed of skills acquisition, proficiency, accuracy and a less steep learning curve.This performance advantage was sustained for at least 6 weeks.Conclusions: TDCS may be a useful, safe adjunct for surgical simulation training.It is associated with improved skills acquisition in both laparoscopic and neurosurgical training tasks.Further research is needed to evaluate its use in other surgical specialties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".