Change in Gustilo-Anderson classification at time of surgery does not increase risk for surgical site infection in patients with open fractures: A secondary analysis of a multicenter, prospective randomized controlled trial
Bibliographic record
Abstract
Abstract Introduction: Open fractures represent a major source of morbidity. Surgical site infections (SSIs) after open fractures are associated with a high rate of reoperations and hospitalizations, which are associated with a lower health-related quality of life. Early antibiotic delivery, typically chosen through an assessment of the size and contamination of the wound, has been shown to be an effective technique to reduce the risk of SSI in open fractures. The Gustilo-Anderson classification (GAC) was devised as a grading system of open fractures after a complete operative debridement of the wound had been undertaken but is commonly used preoperatively to help with the choice of initial antibiotics. Incorrect preoperative GAC, leading to less aggressive initial management, may influence the risk of SSI after open fracture. The objectives of this study were to determine (1) how often the GAC changed from the initial to definitive grading, (2) the injury and patient characteristics associated with increases and decreases of the GAC, and (3) whether a change in GAC was associated with an increased risk of SSI. Methods: Using data from the FLOW trial, a large multicenter randomized study, we used descriptive statistics to quantify how frequently the GAC changed from the initial to definitive grading. We used regression models to determine which injury and patient characteristics were associated with increases and decreases in GAC and whether a change in GAC was associated with SSI. Results: Of the 2420 participants included, 305 participants had their preoperative GAC change (12.6%). The factors associated with upgrading the GAC (from preoperative score to the definitive assessment) included fracture sites other than the tibia, bone loss at presentation, width of wound, length of wound, and skin loss at presentation. However, initial misclassification of type III fractures as type II fractures was not associated with an increased risk of SSI (P = 0.14). Conclusions: When treating patients with open fracture wounds, surgeons should consider that 12% of all injuries may initially be misclassified when using the GAC, particularly fractures that have bone loss at presentation or those located in sites different than the tibia. However, even in misclassified fractures, it did not seem to increase the risk of SSI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".