The Cost of the “July Effect” in Microsurgery
Bibliographic record
Abstract
The existence of the "July effect," or the idea that the new academic year intrinsically has an increased complication rate is evaluated in microsurgical free tissue transfer procedures. The National Surgical Quality Improvement Program registry was queried for all free flap procedures performed between 2005 and 2016 (n = 3405). Cases were grouped as having occurred in the first academic quarter (Q1: July 1-September 30) or fourth quarter (Q4: April 1-June 30). Demographical data and complications were compared using univariate χ analysis, multivariate logistic regression was used to control for confounding variables, and inpatient stay and operating cost estimates were created. Of a total of 1722 cases, 905 were performed in the first academic quarter and 817 were performed in the fourth academic quarter. There was no significant difference between Q1 and Q4 in readmission rate (P = 0.378) or reoperation rate (P = 0.730). Patients in Q1 had significantly longer operative times (P = 0.001) and length of stay (P = 0.002) compared with those in Q4. In addition, cost of inpatient stay and operating costs associated with each free flap were significantly increased in Q1 compared with Q4 (P = 0.029; P = 0.001). The total cost per quarter for free flaps was also significantly more expensive in Q1 vs Q4, with the highest average difference in cost of $350,010.64 (P = 0.001). Having surgery early in the academic year does not put patients at any increased risk for major complications but is associated with increased operating time, length of stay, and total cost.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".