Arthroscopic reduction and internal fixation of capitellar and trochlear fractures: A case series
Bibliographic record
Abstract
Background: In a simple isolated capitellar/trochlear fracture without extensive posterior comminution, arthroscopic reduction and internal fixation (ARIF) can provide an alternative option to open reduction internal fixation. The purpose of this retrospective case series was to report on the technique and outcomes of arthroscopic reduction and internal fixation of capitellar/trochlear fractures. Methods: All patients that underwent ARIF at a single upper extremity referral centre in the last twenty years were reviewed. Patient demographics, preoperative, intraoperative, and postoperative records were obtained through chart review and telephone followup. Results: Ten cases of ARIF were identified over a twenty year period performed by two surgeons. The average age of patients was 37 years (17-63 years), with nine females and one male. With an average followup of eight years, nine of ten patients had a mean range of motion from 0 to 142 degrees. Their average MEPI and PREE score were 93 ± 7 and 8 ± 14, respectively. Four patients had focal cartilage collapse with three that required a reoperation. There were no infections, nonunions, or arthroscopy related complications. Conclusion: ARIF offers an alternative to ORIF for capitellar/trochlear fractures producing good results while providing better visualization of the fracture reduction and minimizing soft tissue dissection.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".