Perception of Plastic Surgery Among Medical Students
Bibliographic record
Abstract
Introduction: Misconception of plastic surgery as a purely aesthetic specialty is common among medical students and interns. This study aims to assess the perception of medical students and interns on plastic surgery in Saudi Arabia and Canada. Materials and Methods: A cross-sectional study was conducted on February 2020 targeting first to last year medical students and interns in Saudi Arabia (IAU, KAUH) and Canada (McGill). Convenient sampling technique was used. Online based and paper questionnaires consisting of 21 questions addressing participants’ demographics as well as a list of various medical scenarios that require participants to choose the most appropriate specialty to refer to. These scenarios aim to assess the broad understanding of the participants of the various pathologies treated by plastic surgeons. Results: A total of 729 students were included in this study, where 27.5% of them were second year medical student, 25.3% were third year, 14.4% fourth year, 11.2% interns and 21.6% other levels (Table 3). Geographical distribution demonstrates that 86.8% of the students were from Saudi Arabia and 13.2% were from Canada (Table 1). The data analysis demonstrated that only 3 questions of the plastic surgery were answered correctly including liposuction (73.7% correct answers), facial sagging (67.2%) and congenital adhesion (52.5%) as shown in Figure 1. The rest of the 9 questions were answered incorrectly, indicating that most of the students misinterpret the various services that plastic surgeons can offer. Conclusion: This data suggests that plastic surgery is still commonly perceived as a merely aesthetic specialty among medical students. We recommend increasing the exposure to plastic surgery for medical students during clinical years to enhance their awareness about the armamentarium of the plastic surgeon.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".