Effect of screw position on bone tissue differentiation within a fixed femoral fracture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plate and screw constructs are routinely used in the treatment of long bone fractures. Despite considerable advancements in technology and techniques, there can still be complications in the healing of long bone fractures. Non-unions, delayed unions, and hardware failures are common complications observed in clinical practice following open reduction and internal fixation of fractures [1]. Potential causes of these adverse clinical effects may be disruptive to the periosteal and endosteal blood supply, stress shielding effects, and inadequate mechanical stability. The goal of the present study was to explore the effect of screw position on the fracture healing and formation of new bone tissue with mechanoregulatory algorithms in a computational model. An idealized poroelastic 3D finite element (FE) model of a femur with a 5 mm fracture gap, including a plate-screw construct was developed. Nineteen different plate-screw combinations, created by varying the number and position of screws within the plate, were created to identify a construct with the most favourable attributes for fracture healing. The first phase of the study evaluated constructs through mechanical stress analyses to identify those constructs with high loadsupport capability. The second phase of the study evaluated healing and bone formation with a biphasic mechanoregulatory algorithm to simulate tissue differentiation for fixation within selected constructs. The results of our analysis demonstrated a 4-screw symmetrical construct with the largest distance between screws to provide the most favourable balance of stability and optimized conditions to promote fracture healing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it