Biomechanical Concepts for Fracture Fixation
Bibliographic record
Abstract
Application of the correct fixation construct is critical for fracture healing and long-term stability; however, it is a complex issue with numerous significant factors. This review describes a number of common fracture types and evaluates their currently available fracture fixation constructs. In the setting of complex elbow instability, stable fixation or radial head replacement with an appropriately sized implant in conjunction with ligamentous repair is required to restore stability. For unstable sacral fractures with vertical or multiplanar instabilities, "standard" iliosacral screw fixation is not sufficient. Periprosthetic femur fractures, in particular Vancouver B1 fractures, have increased stability when using 90/90 fixation versus a single locking plate. Far cortical locking combines the concept of dynamization with locked plating to achieve superior healing of a distal femur fracture. Finally, there is no ideal construct for syndesmotic fracture stabilization; however, these fractures should be fixed using a device that allows for sufficient motion in the syndesmosis. In general, orthopaedic surgeons should select a fracture fixation construct that restores stability and promotes healing at the fracture site, while reducing the potential for fixation failure.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".