Consecutive Microsurgical Cases Performed by Single Surgeon at a Canadian Tertiary Care Center: A Retrospective Review
Bibliographic record
Abstract
Background: The practice of a microsurgeon evolves over time with experience, changes in clinical interest, and practice setting. Previous reports suggest that complication rates may be influenced by years of practice. The aim of this study was to analyze consecutive microsurgical cases performed by a single surgeon during the first half of their career in a broad microsurgical practice at a Canadian academic tertiary care center. Methods: A retrospective chart review of all free tissue transfers between 2007 and 2020 completed by a single academic plastic surgeon was performed. The primary outcomes were the odds of postoperative complications and free flap failure. The secondary outcomes included the annual number and type of free flap cases over time. Results: The surgeon performed 795 free flaps on 537 patients. There were a total of 131 postoperative complications. There was no statistically significant association between the years in practice and odds of a complication. Overall, there were 26 flap failures, yielding a 96.7% success rate. There was no association between flap failure and years in practice. The most common type of reconstruction was breast, followed by lower extremity and head and neck. There was a significant change in the type of reconstruction cases over time ( P < 0.005). Conclusion: The type and volume of free flap reconstruction conducted by the surgeon has changed over time. In this single surgeon career, surgical experience did not have a significant impact on free flap complications. At our center, free tissue transfer remains a valuable tool for reconstruction in both low and high-risk patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".