Training in Trauma Surgery
Bibliographic record
Abstract
OBJECTIVE: To describe outcomes from a clinical trauma surgical education program that places the board-eligible/board-certified fellow in the role of the attending surgeon (fellow-in-exception [FIE]) during the latter half of a 2-year trauma/surgical critical care fellowship. SUMMARY BACKGROUND DATA: National discussions have begun to explore the question of optimal methods for postresidency training in surgery. Few objective studies are available to evaluate current training models. METHODS: We analyzed provider-specific data from both our trauma registry and performance improvement (PI) databases. In addition, we performed TRISS analysis when all data were available. Registry and PI data were analyzed as 2 groups (faculty trauma surgeons and FIEs) to determine experience, safety, and trends in errors. We also surveyed graduate fellows using a questionnaire that evaluated perceptions of training and experience on a 6-point Likert scale. RESULTS: During a 4-year period 7,769 trauma patients were evaluated, of which 46.3% met criteria to be submitted to the PA Trauma Outcome Study (PTOS, ie, more severe injury). The faculty group saw 5,885 patients (2,720 PTOS); the FIE group saw 1,884 patients (879 PTOS). The groups were similar in respect to mechanism of injury (74% blunt; 26% penetrating both groups) and injury severity (mean ISS faculty 10.0; FIEs 9.5). When indexed to patient contacts, FIEs did more operations than the faculty group (28.4% versus 25.6%; P < 0.05). Death rates were similar between groups (faculty 10.5%; FIEs 10.0%). Analysis of deaths using PI and TRISS data failed to demonstrate differences between the groups. Analysis of provider-specific errors demonstrated a slightly higher rate for FIEs when compared with faculty when indexed to PTOS cases (4.1% versus 2.1%; P < 0.01). For both groups, errors in management were more common than errors in technique. Twenty-one (91%) of twenty-three surveys were returned. Fellows' feelings of preparedness to manage complex trauma patients improved during the fellowship (mean 3.2 prior to fellowship versus 4.5 after first year versus 5.8 after FIE year; P < 0.05 by ANOVA). Eighty percent rated the FIE educational experience "great -5" or "exceptional- 6." Eighty-five percent consider the current structure of the fellowship (with FIE year) as ideal. Ninety percent would repeat the fellowship. CONCLUSION: The educational experience and training improvement offered by the inclusion of a FIE period during a trauma fellowship is exceptional. Patient outcomes are unchanged. The potential for an increased error rate is present during this period of clinical autonomy and must be addressed when designing the methods of supervision of care to assure concurrent senior staff review.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".