Effects of shoulder distraction on canine shoulder arthroscopy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the use of the Leipzig distractor during canine shoulder arthroscopy. STUDY DESIGN: Experimental, ex vivo. SAMPLE POPULATION: Paired shoulder joints from 15 large breed canine cadavers. METHODS: Standard lateral shoulder arthroscopy was performed with or without the use of the Leipzig distractor (n = 15 each). Joint space width, procedure time, and visibility and palpability of the intra-articular structures were assessed during the arthroscopy. After the arthroscopic evaluation, each shoulder joint was disarticulated to assess the area and number of iatrogenic articular cartilage injury (IACI) lesions. Sites around the distraction device were assessed for the presence of iatrogenic injury. RESULTS: lower (P = .003), and there were two fewer total number of IACI lesions (P = .004) compared with nondistracted shoulders. The mean total surgery time was 93 seconds shorter (P = .01) in distracted shoulders. Although distraction was associated with increased visibility of the supraglenoid tubercle (P = .015), no significant differences were found for other intra-articular structures for their visibility and palpability. Unexpected lesions at the sites around the distraction device were not encountered. CONCLUSION: Use of the distraction device decreased the area and incidence of IACI lesions and shortened the arthroscopy time. However, no improvement was found in the visibility or palpability of the intra-articular structures. CLINICAL SIGNIFICANCE: Although additional clinical studies are required to evaluate the effect of the distraction device on pathologic articular conditions and intraoperative manipulation, the use of a shoulder distraction device might improve the outcomes of shoulder arthroscopy by decreasing IACI and arthroscopy time.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".