Serum Mast Cell Tryptase as a Marker of Posttraumatic Joint Contracture in a Rabbit Model
Bibliographic record
Abstract
OBJECTIVES: Mast cells have been identified as key mediators of posttraumatic joint contracture, and stabilizing medications (ketotifen) have been shown to decrease contracture severity. Serum mast cell tryptase (SMCT) levels are used clinically to monitor mast cell-mediated conditions. The goals of this study were to determine if SMCT levels are elevated in the setting of joint contracture, if they can be decreased in association with ketotifen therapy, and if they correlate with contracture severity. METHODS: This study used a previously developed rabbit model in which 39 animals were divided into 4 groups: operatively created joint contracture (ORC, n = 13), operatively created contracture treated with ketotifen at 2 doses (KF0.5, n = 9; KF1.0, n = 9), and healthy rabbits (NC, n = 8). Range of motion measures were performed at 8 weeks after the surgery. Serum samples were collected on postoperative days 1, 3, 5, 7, 21, 35, and 49. SMCT levels were measured using a rabbit-specific enzyme-linked immunosorbent assay. RESULTS: Levels of SMCT were highest in the operatively created joint contracture group and were significantly greater compared with both ketotifen groups (P < 0.001). Levels were highest at postoperative day 1 with a trend to decrease over time. A positive correlation between SMCT levels and contracture severity was observed in all operative groups (P < 0.05). CONCLUSIONS: Levels of SMCT are elevated in the setting of joint contracture, decreased in association with ketotifen therapy, and positively correlated with contracture severity. This is the first study to establish a relationship between SMCT and joint injury. Measurement of SMCT may be valuable in identifying those at risk of posttraumatic joint contracture.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".