Plastic surgeons’ self-reported operative infection rates at a Canadian academic hospital
Bibliographic record
Abstract
BACKGROUND: Surgical site infection rates are of great interest to patients, surgeons, hospitals and third-party payers. While previous studies have reported hospital-acquired infection rates that are nonspecific to all surgical services, there remain no overall reported infection rates focusing specifically on plastic surgery in the literature. OBJECTIVE: To estimate the reported surgical site infection rate in plastic surgery procedures over a 10-year period at an academic hospital in Canada. METHODS: A review was conducted on reported plastic surgery surgical site infection rates from 2003 to 2013, based on procedures performed in the main operating room. For comparison, prospective infection surveillance data over an eight-year period (2005 to 2013) for nonplastic surgery procedures were reviewed to estimate the overall operative surgical site infection rates. RESULTS: A total of 12,183 plastic surgery operations were performed from 2003 to 2013, with 96 surgical site infections reported, corresponding to a net operative infection rate of 0.79%. There was a 0.49% surgeon-reported infection rate for implant-based procedures. For non-plastic surgery procedures, surgical site infection rates ranged from 0.04% for cataract surgery to 13.36% for high-risk abdominal hysterectomies. DISCUSSION: The plastic surgery infection rate at the study institution was found to be <1%. This rate was equal to, or somewhat less than, surgical site infection rates. However, these results do not report patterns of infection rates germane to procedures, season, age groups or sex. To provide more in-depth knowledge of this topic, multicentre studies should be conducted.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".