Alterations in thyroid hormone concentrations in healthy sled dogs before and after athletic conditioning
Bibliographic record
Abstract
OBJECTIVE: To determine effects of athletic conditioning on thyroid hormone concentrations in a population of healthy sled dogs. ANIMALS: 19 healthy adult sled dogs. PROCEDURE: Serum concentrations of thyroxine (T4), triiodothyronine (T3), thyroid-stimulating hormone (TSH), free T4 (fT4), free T3 (fT3), and autoantibodies directed against T3, T4, and thyroglobulin were measured in sled dogs that were not in training (ie, nonracing season) and again after dogs had been training at maximum athletic potential for 4 months. RESULTS: Analysis revealed significant decreases in T4 and fT4 concentrations and a significant increase in TSH concentration for dogs in the peak training state, compared with concentrations for dogs in the untrained state. Serum concentrations of T4 and fT4 were less than established reference ranges during the peak training state for 11 of 19 and 8 of 19 dogs, respectively; fT4 concentration was greater than the established reference range in 9 of 19 dogs in the untrained state. CONCLUSIONS AND CLINICAL RELEVANCE: Decreased total T4 and fT4 concentrations and increased serum concentrations of TSH were consistently measured during the peak training state in healthy sled dogs, compared with concentrations determined during the untrained state. Although thyroid hormone concentrations remained within the established reference ranges in many of the dogs, values that were outside the reference range in some dogs could potentially lead to an incorrect assessment of thyroid status. Endurance training has a profound impact on the thyroid hormone concentrations of competitive sled dogs.
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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 | 0.001 | 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".