Bibliographic record
Abstract
The traditional form of surgical skills training and recent changes in health care have created challenges in keeping up the standards in skills training of future surgeons. The structured development of simulation training might help tackle these challenges. The main aim of this thesis was to explore whether basic surgical skills acquired using proficiency-based simulation training in superficial femoral artery (SFA) angioplasty and saphenofemoral junction (SFJ) dissection translate to real-world performance. Four studies were performed. In the first study, a procedure-specific checklist for SFA angioplasty was developed and validated using the Vascular Intervention Simulation Trainer (VIST) simulator. In the second study, the impact of an assistant on the technical skills of the primary operator performing SFA angioplasties on the VIST simulator was assessed. The first and the second studies were essential to study the transfer of endovascular skills after proficiency-based simulation training in SFA angioplasty to the interventional suite (third study). The fourth study describes the transfer of open vascular surgical skills after proficiency-based bench model simulation training in SFJ dissection to the operating room (OR). Simulation-trained trainees scored higher than the controls on the procedural checklist developed (86.80 ± 5.36 vs. 67.60 ± 6.02 P = 0.001) and a global rating scale (37.20 ± 4.09 vs. 24.40 ± 5.32 P = 0.003) when performing SFA angioplasty on patients. Similarly, bench model simulation-trained trainees scored higher than the controls on procedural (30.33 ± 2.07 vs. 18 ± 2.19 P < 0.001) and global (28.33 ± 1.86 vs. 18.50 ± 4.04 P < 0.001) rating scales when performing SFJ dissection on patients. Basic surgical skills acquired using proficiency-based simulation training in SFA angioplasty and SFJ dissection do translate to real world performance. Structured proficiency-based simulation training in SFA angioplasty and SFJ dissection should be incorporated into surgical training programs.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".