Characterization of a Pre-Clinical Mini-Pig Model of Scaphoid Non-Union
Bibliographic record
Abstract
A fractured scaphoid is a common disabling injury that is frequently complicated by non-union. The treatment of non-union remains challenging because of the scaphoid's small size and delicate blood supply. Large animal models are the most reliable method to evaluate the efficacy of new treatment modalities before their translation into clinical practice. The goal of this study was to model a human scaphoid fracture complicated by non-union in Yucatan mini-pigs. Imaging and perfusion studies were used to confirm that the anatomy and blood supply of the radiocarpal bone in mini-pigs were similar to the human scaphoid. A 3 mm osteotomy of the radiocarpal bone was generated and treated with immediate fixation or filled with a dense collagen gel followed by delayed fixation. Bone healing was assessed using quantitative micro computed tomography and histology. With immediate fixation, the osteotomy site was filled with new bone across its whole length resulting in complete bridging. The dense collagen gel, previously shown to impede neo-vascularization, followed by delayed fixation resulted in impaired bridging with less bone of lower quality. This model is an appropriate, easily reproducible model for the evaluation of novel approaches for the repair of human scaphoid fractures.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".