Do Microsurgical Outcomes Differ Based on Which Specialty Does the Operation? A NSQIP Analysis
Bibliographic record
Abstract
Background: Because plastic surgeons do not “own” a specific anatomic region, other surgical specialties have increasingly assumed procedures historically performed by plastic surgery. Decreased case volume is postulated to be associated with higher complication rates. Herein, we investigate whether volume and surgical specialty have an impact on microsurgical complications, specifically surgical site infection (SSI) and reoperation rates. Methods: The 2005–2015 National Surgical Quality Improvement Program participant use file was queried by Current Procedural Terminology code for breast and head/neck microsurgeries. Multivariate logistic regression was performed to compare the outcomes between surgical specialties. A cumulative frequency variable was introduced to investigate the effect of case volume on complication rates. Results: We captured 6,617 microsurgical cases. Multivariate logistic regression revealed that although the rate of SSI was lower in plastic surgery compared with otolaryngology for head and neck reconstructions (13.3% versus 10.5%) and compared with general surgery for breast reconstructions (5.4% versus 4.7%), there was no significant difference between specialties (P = 0.13; P = 0.96). Increased case volume is negatively correlated with complications. Conclusions: Plastic surgery is at risk given case cannibalization by other specialties. We conclude that surgical specialty does not affect the rates of SSI and reoperation. We demonstrate a correlation between lower volumes and increased complications, implying that, once a specialty has amassed critical case experience, complication rates may decrease, and outcomes can be equivalent or superior. Case breadth and volumes should be maintained to preserve skills, optimize outcomes, and maintain the specialty as it currently exists.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".