Advanced Trauma Operative Management Course: Participant Survey
Bibliographic record
Abstract
BACKGROUND: The Advanced Trauma Operative Management (ATOM) course uses standardized porcine simulation to teach the repair of penetrating trauma. It is offered in 26 sites in the United States, Canada, Africa, the Middle East, and Japan. The purpose of the present study was to query ATOM participants regarding their perceptions of the value and influence of the ATOM course on knowledge, confidence, and skill to repair penetrating injuries. METHODS: An anonymous, voluntary survey was posted on the Internet at surveymonkey.com. E-mail notification was sent to all 1,001 ATOM participants through May 2008. Items requested agreement/disagreement on a 5-point Likert scale and space for comments. Agreement indicated positive perceptions of ATOM. RESULTS: A total of 962 surgeons received the request to complete the survey; 444 ATOM participants from 36 states and 17 countries participated, for a response rate of 46%. Range of agreement with all of the items was 75.4-99.0%. Results include the following: 78.9% (95% CI, 74.7-82.6%) can identify injuries more quickly; 80.7% (95% CI, 76.6-84.3%) have a more organized operative approach; 81.1% (95% CI, 77.0-84.6%) can control bleeding more quickly; 86.1% (95% CI, 82.4-89.2%) can control injuries more effectively; 86.4% (95% CI, 82.7-89.4%) are more competent trauma surgeons; 87.0% are more confident (95% CI, 83.4-89.9%), and 89.2% are more knowledgeable (95% CI, 85.8-91.8%) about repairing penetrating injuries; 99% (95% CI, 97.4-99.7%) said ATOM is worthwhile. Overall, 87.4% of the comments were positive. CONCLUSIONS: Participants worldwide perceive that ATOM is worthwhile and helps surgeons improve knowledge, confidence, and skill in repairing penetrating injuries.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".