Vascularity evaluation of the equine tarsocrural and proximal intertarsal joint septum, and comparative analysis of two arthroscopic transection techniques
Bibliographic record
Abstract
OBJECTIVE: To evaluate vascularity of the synovial membrane covered septum (SMS) separating the tarsocrural (TC) and proximal intertarsal (PIT) joints (Part 1) and compare two methods of transection, electrosurgical or Ferris Smith rongeur (FS rongeur) (Part 2). STUDY DESIGN: Experimental study. SAMPLE POPULATION: Part 1, 10 SMS (n = 5 horses). Part 2, six horses (n = 12 tarsi). METHODS: In part 1, SMS harvested postmortem were each divided into eight regions of interest (ROIs), processed for histology, and immunostained with anti-α-actin antibody for blood vessel identification. Vascular density was calculated for each ROI. Data was compared within and between horses. In part 2, six horses underwent TC arthroscopy. Each limb was randomly assigned to undergo either electrosurgical or FS rongeur SMS transection. SMS transection and total operative time were recorded. Intraoperative hemorrhage was scored. Data was compared between both techniques. RESULTS: Significant interindividual variations in SMS vascular density were detected (p = .02), but there were no differences among ROIs. No differences in the transection time were detected between electrosurgery (4.83 ± 0.54 min) and FS rongeur (4.33 ± 0.67 min). No differences were found in intraoperative hemorrhage scores between techniques. CONCLUSION: Vascularity within the SMS varies among horses but not within its regions. Electrosurgical or FS rongeur transection of the medial SMS during tarsocrural arthroscopy is a rapid technique and improves surgical access to the dorsal compartment of the PIT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".