So the Bone Flap Hit the Floor, Now What? An In Vitro Comparison of Cadaveric Bone Flap Decontamination Procedures
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Over the course of their career, 66% of neurosurgeons will witness someone accidentally dropping a bone flap on the floor during a craniotomy procedure. Although this event is rare, it can have significant consequences for the patient, and little literature is available to guide management of this complication. Our objective was to compare 5 bone flap decontamination protocols for efficacy in reducing bacterial load, with the goal of safely reimplanting the dropped flap. METHODS: Cadaveric human bone flaps were contaminated with common operating room (OR) contaminant bacteria. The bone flaps were then subject to 1 of 5 decontamination protocols: washing in saline, mechanical debridement, washing in antibiotics, washing in alcoholic chlorhexidine antiseptic, and flash decontamination in autoclave. Inoculum from the flaps was then used to grow bacteria in petri dishes, and bacterial load after decontamination was assessed. Some flaps were physically dropped on an OR floor to simulate and evaluate a real-life contamination. RESULTS: The observed contamination from a flap dropped on an OR floor can be significant (up to 1070 colony-forming units cultured per flap). All protocols tested decreased bacterial load of the bone flaps to different degrees: saline by 95.7%, mechanical debridement by 97.5%, antibiotic bath by 99.5%, alcoholic chlorhexidine by 99.9%, and flash sterilization by 100.0%. Flash sterilization led to significant alterations in the flap's physical appearance. CONCLUSION: In the event of the accidental fall of a bone flap, decontamination by rinsing in an alcohol-chlorhexidine solution followed by 3 successive washes in saline seemed to provide the best balance between effectiveness, safety, and complexity of the method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".