Evaluating the July Phenomenon in Plastic Surgery: A National Surgical Quality Improvement Program Analysis
Bibliographic record
Abstract
BACKGROUND: The perception that complications are more frequent earlier in the medical academic year, known as the "July phenomenon," has been studied in several specialties, with conflicting results. This phenomenon has yet to be studied in plastic surgery; therefore, this study sought to evaluate the presence of the July phenomenon within plastic surgery. METHODS: The American College of Surgeons National Surgical Quality Improvement Program database was accessed, and cases from 2005 to 2014 where "plastic surgery" was listed as the surgical specialty were identified. Only cases with trainee involvement were included for analysis. Included cases were stratified into two groups based on calendar-year quarter of admission. The quarter-3 group included 2451 cases performed during July to September of each calendar year, and the remaining-quarters group included 7131 cases performed in the remaining quarters of each calendar year. Complication rates for 24 complications of interest for quarter-3 and remaining-quarters cases with trainee involvement were calculated, chi-square analysis was used to compare complication rates between groups. Multivariate regression analysis was performed to control for potential confounders. RESULTS: Comparison of complication rates within operations with trainee involvement showed a statistically significant increase in quarter-3 versus remaining-quarters groups for superficial wound infection (0.032 versus 0.023; p = 0.046) and wound dehiscence (0.010 versus 0.006; p = 0.034). No significant difference was found for the remaining 22 complications evaluated. CONCLUSION: This study of a nationwide surgical database found that for the vast majority of complications coded in the database, the rates do not increase in the beginning of the academic year. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".