Impact of the “July effect” in head and neck microvascular reconstruction: A retrospective review
Bibliographic record
Abstract
Abstract Objective The “July effect,” a theory that the beginning of the academic year has worse operative outcomes and complication rates, remains controversial. We evaluated the “July effect” as a risk factor for negative operative outcomes in head and neck microvascular reconstruction. Methods Multi‐institutional retrospective review at three academic tertiary care centers from January 2010 to August 2021. Free flaps were stratified by the academic quarter. Patient factors, operative variables, length of stay (LOS), flap failures, and postoperative complications and adverse events were compared between academic quarters 1 and 4. Results We identified 2897 free flaps: 749 quarter 1 (Q1), 693 quarter 2 (Q2), 770 quarter 3 (Q3), and 685 quarter 4 (Q4). Overall flap failure rate was 4.9% ( n = 143), and the most common postoperative complications were wound infection (12.8%, n = 370) and dehiscence (7.6%, n = 221). There were no significant differences between quarters in overall complication rate, flap failures, partial flap necrosis, wound infection, fistula, or hematoma ( p > 0.05). There were also no significant differences in LOS or rates of 30‐day readmission or reoperation ( p > 0.05). Q1 had significantly more dehiscences ( p = 0.04) and longer operative times ( p = 0.001) than Q4. Conclusion Although Q1 surgeries had significantly longer operative times and higher dehiscence rates, we found no other differences in postoperative complications, flap failures, or adverse events by the academic quarter. While a “July effect” may exist due to the integration of new trainees into the surgical workflow, this effect does not translate into meaningful differences in overall free flap or patient outcomes.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".