Arthroscopy‐assisted fracture fixation
Bibliographic record
Abstract
PURPOSE: the purpose of this article was to systematically analyze the results of published studies in the literature which evaluated the use of arthroscopically assisted techniques in intra-articular fracture fixation. METHODS: published investigations to date were analyzed by classifying them according to joints that were involved with intra-articular fractures including: knee, ankle, hip, shoulder, elbow, and wrist joints. The results were studied to assess the feasibility, efficiency, and outcomes of arthroscopy-assisted fracture fixation. RESULTS: arthroscopy-assisted techniques have been used successfully for the treatment of fractures of the tibial plateau, tibial eminence, malleoli, pilon, calcaneus, femoral head, glenoid, greater tuberosity, distal clavicle, radial head, coronoid, distal radius, and scaphoid. The major advantages of arthroscopic fracture fixation over open methods are direct visualization of the intra-articular space, decreased invasiveness, and the possibility for multitask interventions through which fixation of the fracture, and repair of the soft tissues and the cartilage can be performed simultaneously. The time-consuming and technically demanding nature of the procedures with a prolonged learning curve and limited fixation alternatives are the main disadvantages of this technique. CONCLUSION: arthroscopic fixation is increasingly utilized for certain intra-articular fracture types due to the minimally invasive nature of the procedures and high accuracy. Randomized controlled trials are needed to justify wider use of arthroscopy-assisted techniques for treatment of intra-articular fractures.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".