Effect of dental floating on the rostrocaudal mobility of the mandible of horses
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of dental floating on the position of the mandible relative to the maxilla (a measure of rostrocaudal mobility [RCM] of the mandible) during extension and flexion of the head of horses. DESIGN: Randomized controlled blinded trial. ANIMALS: 59 horses housed in 1 barn. PROCEDURE: Horses were formally randomized into a treatment (n = 33) or control (26) group. All horses were sedated, and the distance between rostral portions of the upper and lower incisor arcades were determined with the head fully extended and flexed at the poll (the difference in measurements represented the RCM of the mandible). The oral cavity was examined. For the treatment group, dental floating was performed, and the incisor arcade measurements were repeated. RESULTS: Dental correction resulted in a significant increase in RCM of the mandible in 31 of 33 horses. The mobility was greater in heavy horses than that detected in other breed classifications. Age and number of dental lesions did not significantly affect mobility before or after dental floating. CONCLUSIONS AND CLINICAL RELEVANCE: Dental floating increased RCM of the mandible, but measurement of this variable was not an indicator of the number or extent of dental lesions, and no specific dental abnormality appeared to significantly affect RCM of the mandible in horses. In horses, measurement of RCM of the mandible can be used as a guide to determine whether dental correction is necessary; after dental floating, it can be used to ensure that irregularities of the occlusal surface have been corrected.
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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) | 0.001 | 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) | 0.002 | 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".