Launching the Advanced Trauma Operative Management (ATOM), Course in Trinidad and Tobago
Bibliographic record
Abstract
Background: The Advanced Trauma Operative Management (ATOM) course which is aimed at improving penetrating trauma management skills is very challenging to conduct. We assessed the feasibility and potential impact of ATOM training in Trinidad and Tobago through the University of Toronto Global surgery initiative and its potential for improving penetrating trauma care in this developing country. Methods: Senior General Surgical trainees were randomly assigned to participate. Other participants consisted of: an experienced international ATOM course Director, one experienced ATOM instructor, an ATOM instructor candidate, an experienced ATOM veterinary medicine technologist, 2 veterinary medicine trainees, 8 senior general surgical trainees who completed the course using the 2 student to one faculty training model. Pre and post course self efficacy scores (a measure of confidence in surgical approach) and scores on multiple choice question exams (MCQ) were compared by paired t tests. The trainees completed 5-point Likert scales to assess different components of the course. Results: The course was successfully completed locally. Mean and SD self efficacy scores improved from 55.4 ± 18.5 to 80.5 ± 9.1 and MCQ improved from 63.0 ± 8.8 to 82.5 ± 9.6 (p Conclusions: Based on trainee course performance and their evaluations, there was significant improvement in trauma skills, knowledge and attitude with enthusiastic support for continuing the program, to improve penetrating trauma care locally and extending this training to other parts of the Caribbean.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".