Serum Biochemistry and Inflammatory Cytokines in Racing Endurance Sled Dogs With and Without Rhabdomyolysis
Bibliographic record
Abstract
Serum muscle enzymes in endurance sled dogs peak within 2-4 days of racing. The object of this study was to compare mid-race serum chemistry profiles, select hormones, and markers of inflammation and the acute phase response in dogs that successfully completed half of the 2015 Yukon Quest sled dog race (n = 14) and those who developed clinical exertional rhabdomyolysis (ER) (n = 5). Concentrations of serum phosphorus in ER dogs were moderately elevated compared to healthy dogs (median 5.5 vs. 4.25 mg/dL, P = 0.001) at mid race. ALT, AST, and CK show a marked increase from pre-race baseline to mid-race chemistries (P < 0.01), with more pronounced increases in dogs with ER compared to healthy racing dogs (median 46,125 vs. 1,743 U/L; P < 0.001). Potassium concentrations were moderately decreased from pre-race baselines in all dogs (median 5.1 vs. 4.5mEq/L; P < 0.01), and even lower in dogs with ER (median 3.5 mEq/L; P < 0.001) mid-race. No changes in serum pro-inflammatory cytokine concentrations were noted in any groups of dogs. C-reactive protein was elevated in both groups of dogs, but significantly higher in those with ER compared with healthy dogs mid-race (median 308 vs. 164 ug/mL; P < 0.01). Dogs with clinical ER may exhibit CK elevations of 50 times the upper limits of normal, while healthy dogs may have CK elevations over 10,000 U/L. Although potassium decreases in healthy endurance sled dogs, it remains in the normal laboratory reference range; however ER dog potassium levels drop further to the point of hypokalemia. Such an electrolyte disturbance may predispose these dogs to developing ER. Lastly increases in CRP may be reflective of a physiological response to exercise over the course of a race; however high CRP in ER dogs may be capturing an early acute phase response from myonecrosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".