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Record W3133166227 · doi:10.1093/asj/sjab005

Commentary on: Resident Exposure to Aesthetic Surgical and Nonsurgical Procedures During Canadian Residency Program Training

2021· article· en· W3133166227 on OpenAlexaboutno aff
Katherine B. Santosa, Paul S. Cederna

Bibliographic record

VenueAesthetic Surgery Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAbdominoplastyBreast augmentationMastopexyBody contouringSpecialtyPlastic surgeryBreast reductionBlepharoplastySurgeryResidency trainingContouringGeneral surgeryMedical educationFamily medicineContinuing educationEyelid

Abstract

fetched live from OpenAlex

Although aesthetic surgery is a core component of plastic surgery education and training, meaningful exposure to aesthetic surgery remains a challenge for most academic training programs in North America. Not surprisingly, the lack of exposure to aesthetic surgical procedures during training results in graduating residents with lower confidence in performing these procedures as independent surgeons,1,2 which could have an adverse impact on the future of our specialty. In this retrospective analysis of Canadian plastic surgery resident case logs between 2004 and 2014,3 the authors evaluated the level of exposure and confidence in performing aesthetic surgery procedures. Similar to what has been demonstrated in previous studies,1,2,4 the authors found that residents had the greatest exposure to breast and body contouring procedures such as breast augmentation, mastopexy, and abdominoplasty and the least exposure to facial aesthetic procedures such as rhinoplasty and facial rejuvenation....

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)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.998
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0050.001
Research integrity0.0300.018
Insufficient payload (model declined to judge)0.0140.006

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 head0.032
GPT teacher head0.294
Teacher spread0.262 · 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.

Study designNot applicable
DomainIncentives
GenreCommentary

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

Citations1
Published2021
Admission routes1
Has abstractyes

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