Bone Grafting
Bibliographic record
Abstract
Acute fractures, nonunions, and nonunions with bone defects or osteomyelitis often need bone graft to facilitate union. There are several factors to consider when it is determined that a bone graft is needed. These factors include the source of the bone graft (autograft vs. allograft), proper timing for placement of the bone graft, strategies to avoid further complications (particularly in the setting of osteomyelitis), and with the development of a variety of bone graft substitutes, whether alternatives to autograft are available and appropriate for the task at hand. Autograft bone has commonly been referred to as the "gold standard" of bone grafts, against which the efficacy of other grafts has been measured. The best timing for when to place a bone graft or substitute is also somewhat controversial, particularly after an open fracture or a potentially contaminated bed. The treatment of infected nonunions, particularly those that require a graft to facilitate healing, can be quite challenging. Typically, the infection is completely eradicated before placement of a bone graft, but achieving a sterile bed and the timing of a bone graft require strategic thinking and planning. This review outlines the benefits of autografts, the most suitable sites for harvesting bone grafts, the timing of bone graft procedures, the potential risks and benefits of grafting in the face of infection, and the currently available bone graft extenders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".