The Incidence of Post-discharge Surgical Site Infection in the Injured Patient
Bibliographic record
Abstract
BACKGROUND: Approximately 50% of surgical site infections (SSI) after elective surgery occur after discharge. Adequate surveillance for these infections requires a mechanism for post-discharge follow up. The incidence of SSI after injury is as high as 30%. As post-discharge follow up in the trauma population is difficult, we set out to ascertain the incidence of post-discharge SSI in a cohort of high-risk trauma patients. METHODS: Patients (n = 268) enrolled in a randomized controlled trial of leukoreduced versus regular blood transfusions were evaluated either in person or by structured telephone survey 28 days after admission regarding the presence of SSI. Inclusion criteria were age >17 years and blood transfusion within 24 hours of injury. RESULTS: Among the 268 patients, 39 (15%) developed a SSI. There were 27 SSI identified in hospital and 13 identified in the post-discharge period after a median length of stay of 17 days (one patient had more than one SSI). Although the 13 patients who developed a SSI in the post-discharge period comprised only 7% (13 of 194) of the cohort that had at least one operative procedure and survived to discharge, these patients represented 33% (13 of 39) of all patients who developed a SSI. CONCLUSION: Despite their prolonged length of stay compared with elective surgical patients, a significant proportion of SSI after injury occurs after discharge. These data support the need for a post-discharge surveillance system in either clinical trials or for quality assurance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".