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Training in Trauma Surgery

2003· article· en· W2325156352 on OpenAlexfundno aff
Patrick M. Reilly, C. William Schwab, Elliott R. Haut, Vicente H. Gracias, G. Paul Dabrowski, Rajan Gupta, John P. Pryor, Donald R. Kauder

Bibliographic record

VenueAnnals of Surgery · 2003
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsnot available
FundersMcMaster University
KeywordsMedicineTraining (meteorology)SurgeryGeneral surgery

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0300.004

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.

Opus teacher head0.543
GPT teacher head0.390
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations36
Published2003
Admission routes1
Has abstractyes

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