Bibliographic record
Abstract
Abstract In 2005, the American College of Surgeons (ACS) launched an innovative program to accredit simulation centers based on rigorous standards and criteria. These standards and criteria were developed through an intense two‐year process that involved internationally‐renowned experts in the field of surgical simulation and individuals who were leading technical skills training centers in the US and Canada. We pilot‐tested the accreditation model, as well as the standards and criteria to ensure that they were robust and reliable. A few years ago we revised the accreditation model to one of continuing certification with longer accreditation cycles and detailed annual reports and targeted reviews as necessary. The accredited simulation centers are called ACS‐accredited Education Institutes given their broad scope of activities focusing on skills training, verification, and validation. The accreditation model includes two levels – comprehensive and basic, and there are different standards and criteria for each level of accreditation. The program has grown significantly and now includes 92 ACS‐accredited Education Institutes of which 82 are in the US and 10 in other regions of the world, including Canada, UK, Europe, Middle East, and Latin America. The program continues to grow and there is an annual meeting in Spring each year called the ACS Surgical Simulation Summit that provides opportunities to present scientific work, best practices, and latest advances in surgical simulation. In addition to the representatives from the ACS‐accredited Education Institutes, several other experts participate as well. The program continues to support the needs of practicing surgeons, surgery residents, medical students, and members of surgical teams. The ACS Division of Education looks forward to increasing international participation in this unique and one‐of‐a‐kind network.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".