Computer-Assisted Percutaneous Scaphoid Fixation: Concepts and Evolution
Bibliographic record
Abstract
Background The treatment for undisplaced scaphoid waist fractures has evolved from conventional cast immobilization to percutaneous screw insertion. Percutaneous fixation reduces some of the risks of open surgery, but can be technically demanding and carries the risk of radiation exposure. Recently, computer-assisted percutaneous scaphoid fixation (CAPSF) has been gaining interest. Materials and Methods Conventional percutaneous scaphoid fixation is performed under fluoroscopic guidance and involves insertion of a guide wire along the length of the scaphoid to facilitate placement of a cannulated screw. Adapting computer-assisted techniques for scaphoid fixation poses several unique challenges including patient tracking and registration. Results To date, five groups have successfully implemented systems for CAPSF. These systems have implemented wrist immobilization strategies to resolve the issue of patient tracking and have developed unique guidance techniques incorporating 2D fluoroscope, cone-beam CT, and ultrasound, to circumvent patient-based registration. Conclusions Computer-aided percutaneous pinning of scaphoid waist fractures can significantly reduce radiation exposure and has the potential to improve the accuracy of this procedure. This article reviews the rationale for, and the evolution of, CAPSF and describes the key principles of computer-assisted technology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".