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Record W2068017611 · doi:10.1155/2014/281923

Aesthetic Surgery Training during Residency in the United States: A Comparison of the Integrated, Combined, and Independent Training Models

2014· article· en· W2068017611 on OpenAlexaff
Arash Momeni, Rebecca Y. Kim, Derrick C. Wan, Ali Izadpanah, Gordon K. Lee

Bibliographic record

VenuePlastic Surgery International · 2014
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsTraining (meteorology)Residency trainingMedical educationMedicineComputer scienceGeographyContinuing education

Abstract

fetched live from OpenAlex

Background. Three educational models for plastic surgery training exist in the United States, the integrated, combined, and independent model. The present study is a comparative analysis of aesthetic surgery training, to assess whether one model is particularly suitable to provide for high-quality training in aesthetic surgery. Methods. An 18-item online survey was developed to assess residents' perceptions regarding the quality of training in aesthetic surgery in the US. The survey had three distinct sections: demographic information, current state of aesthetic surgery training, and residents' perception regarding the quality of aesthetic surgery training. Results. A total of 86 senior plastic surgery residents completed the survey. Twenty-three, 24, and 39 residents were in integrated, combined, and independent residency programs, respectively. No statistically significant differences were seen with respect to number of aesthetic surgery procedures performed, additional training received in minimal-invasive cosmetic procedures, median level of confidence with index cosmetic surgery procedures, or perceived quality of aesthetic surgery training. Facial aesthetic procedures were felt to be the most challenging procedures. Exposure to minimally invasive aesthetic procedures was limited. Conclusion. While the educational experience in aesthetic surgery appears to be similar, weaknesses still exist with respect to training in minimally invasive/nonsurgical aesthetic procedures.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.300
Teacher spread0.211 · 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 teacher head, 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

Citations28
Published2014
Admission routes1
Has abstractyes

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