The management of fracture related infections: What practices can be supported by high-level evidence?
Bibliographic record
Abstract
Fracture related infections (FRIs) are a disabling condition causing significant concern within the orthopaedic community. FRIs have a huge societal and economic burden leading to prolonged recovery times and the potential for becoming chronic conditions or being life-threatening. Despite its importance in our field, the surgical community has just recently agreed on a definition which, added to the lack of surgical trials assessing preventive and treatment interventions have limited our understanding and precipitated wide variations in surgeons' practice. This article aims to review the current practices that can be supported with high-quality evidence. Currently, we have a limited body of high-quality evidence on FRI prevention and treatment. A handful of measures have proven effective, such as the use of prophylactic antibiotics, the use of saline and low pressure as the preferred irrigation solution and the safety of delaying initial surgical débridement more than 6 hours without impacting infection rates for open fracture wounds débridement. Future multicentre trials, properly powered, will shed light on current areas of controversy regarding the benefit of different preoperative and perioperative factors for the prevention and treatment of FRIs. Higher quality evidence is needed to guide surgeons to offer an evidence-based approach to prevent FRI occurrence and to treat patients suffering from them.
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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 | 0.018 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".