Effect of dental floating on weight gain, body condition score, feed digestibility, and fecal particle size in pregnant mares
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of routine dental floating on weight gain, body condition score, feed digestibility, and fecal particle size in pregnant mares fed various diets. DESIGN: Randomized controlled clinical trial. ANIMALS: 56 pregnant mares. PROCEDURE: Mares were randomly allocated to 1 of 4 feed groups (n = 14 mares/group). All horses were sedated and an oral examination was performed, after which dental floating was performed on 7 horses in each group. Body weight was measured, and a body condition score was assigned before and at various times for 24 weeks after dental floating. Feed digestibility and fecal particle size were analyzed 7 and 19 weeks after dental floating. RESULTS: Weight gain, change in body condition score, feed digestibility, and fecal particle size were not significantly different between horses that underwent dental floating and untreated control horses. In contrast, weight gain was significantly associated with feed group. In the control horses, neither the number of dental lesions nor the presence of any particular type of lesion at the time of the initial oral examination was significantly associated with subsequent feed digestibility. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggest that dental floating does not result in significant short-term changes in body weight, body condition score, feed digestibility, or fecal particle size in healthy pregnant mares. Further studies are necessary to determine the clinical utility of regular dental floating in apparently healthy horses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".