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Summary Report on Current Clinical Trauma Care Fellowship Training Programs

2005· article· en· W2029942577 on OpenAlexaboutno aff
William C. Chiu, Thomas M. Scalea, Michael F. Rotondo

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2005
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurrent (fluid)Training (meteorology)Medical educationMedicinePsychologyEngineeringGeographyElectrical engineering

Abstract

fetched live from OpenAlex

BACKGROUND: Almost 10 years ago, the Careers in Trauma Committee of the Eastern Association for the Surgery of Trauma (EAST) identified four main problems with trauma fellowships: (1) lack of specified educational objectives, (2) undefined curricula, (3) inconsistent emphasis on research, and (4) inconsistent surgical exposure. These perceived problems still exist and may threaten the future of trauma surgery as a career. The objective of this study was to examine these issues in a profile of the current active clinical trauma care fellowship training programs. METHODS: The database foundation was the Trauma Fellowships Listing at the EAST Web site (http://www.east.org). All active clinical trauma training programs on this list were identified, and descriptive information was updated and abstracted. A supplemental survey was sent to each program contact person with specific questions regarding program organization, educational material, fellow responsibilities, and scholarly opportunities. In 2003, the entire database was updated, and the survey process was repeated. RESULTS: The number of active trauma care fellowship programs was 39 (1996), 43 (1999), and 50 (2003). From 1996 to 2003, 15 new programs came into existence, and 4 programs became inactive. Current programs are located in 23 states, Washington DC, Canada, and Australia. California has seven; Pennsylvania has four; and three states have three programs each. The annual trauma admissions for most programs (42 of 50, 84%) vary between 750 and 4,000, with six programs admitting more than 4,000. The most common program format (20 of 50, 40%) offers combined trauma and critical care training, whereas only three programs (6%) offer a choice of trauma only, critical care only, or combined trauma and critical care. A Residency Review Committee (RRC)-approved surgical critical care program was an integral component in 54% (1996), 76% (1999), and 78% (2003). The majority of programs (39 of 50, 78%) are of 1-year duration, with some (22 of 50, 44%) having an optional second year. Most programs (40 of 50, 80%) have one or two positions per year, with the largest program having eight fellows per year. The total number of positions available per year was 66 (1996), 89 (1999), and 95 (2003). Most fellows lead and direct a team of residents and medical students. More programs reported that fellows direct the initial resuscitation of all trauma patients admitted, and more programs are requiring in-house call requirements for fellows. CONCLUSIONS: There is steady growth in trauma fellowship training, with an emphasis on direct clinical management. An RRC-approved surgical critical care program is an important link, but one not essential to the trauma fellowship. Expected radical changes in surgical and trauma training are on the horizon. It is imperative that leaders in trauma surgery continue to monitor these trends for successful integration of trauma care training into surgical residency redesign efforts, and for facilitation of programmatic improvement in trauma care as a career.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0300.007

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 head0.096
GPT teacher head0.422
Teacher spread0.326 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations23
Published2005
Admission routes1
Has abstractyes

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