Mechanical Testing and Computational Modeling of Scapula Fracture Implants: A Review
Bibliographic record
Abstract
This is the first review of mechanical testing or computational modeling articles that analyzed the biomechanical influence of parametrically changing scapula fracture implant (SFI) variables to optimize or characterize performance. PubMed and Embase were explored for articles using inclusion and exclusion criteria: (i) SFI articles that permutated implant variables were included, but articles using only one SFI configuration were excluded; (ii) SFIs comprising of plates (with screws), non-plate isolated "lag" screws, or cerclage wires were included, but anchors/sutures, bone cements, bone grafts, or soft-tissue grafts were excluded; (iii) scapula fractures were included, but adjacent injuries of the clavicle, humerus, labrum/capsule, ligaments/tendons, or muscles were excluded; (iv) cadaveric, synthetic, or computer-generated scapulas were included, but other anatomic sites or biomaterials were excluded; (v) published on any date in any language. The 16 eligible articles considered different scapula regions (i.e., acromion/spine, body, neck), SFI variables (i.e., plate geometry, plate hole type, plate position, plate number, screw angle), and engineering outcome metrics: (i) interfragmentary displacement (i.e., chance of callus growth); (ii) stresses in bones, plates, or screws (i.e., risk of failure); (iii) stresses in the bone under the plate (i.e., risk of bone "stress shielding"); (iv) the number of loading cycles to failure (i.e., fatigue life); (v) overall stiffness (i.e., physical flexibility); or (vi) failure strength (i.e., peak load achieved). This review advises future work for implant variables, bone characteristics, and study protocols, while suggesting SFI configurations with maximum mechanical stability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".