Surgical Experience at the Canadian-Led Role 3 Multinational Medical Unit in Kandahar, Afghanistan
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to document the surgical experience of the Role 3 Multinational Medical Unit (R3MMU) at Kandahar Airfield Base while Canada was the lead nation for the facility. This study will help inform on future staffing, training, and deployment issues of field hospitals on military missions. METHODS: From February 2, 2006, to October 15, 2009, the Canadian Forces Health Services served as the lead nation for the R3MMU. We retrospectively reviewed the electronic and the actual operative database during this timeframe to assess surgical workload, types of surgical procedures performed, and the involved anatomic regions of the surgical procedures. RESULTS: During this timeframe, there were 6,735 operative procedures performed on 4,434 patients. The majority of our patients were Afghan nationals, with Afghan civilians representing 34.8%, Afghan National Security Forces 31.6%, and North Atlantic Treaty Organization forces 25.3%. The number of operative procedures by specialty were 3,329 in orthopedic surgery (49.4%), 2,053 general surgery (30.5%), 930 oral maxillofacial surgery (13.8%), and 272 neurosurgery (6%). The most frequently operated on body region was the soft tissue, followed by the extremities and then the abdomen. Thoracic operations were very infrequent. CONCLUSION: Our operative data were slightly different from historical controls. Hopefully, this data will help with planning for future deployments of field hospitals on military missions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".