Cheek teeth apical infection in cattle: Diagnosis, surgical extraction, and prognosis
Bibliographic record
Abstract
OBJECTIVE: To report the clinical presentation, treatment, and outcome of cattle undergoing surgical extraction of apically infected cheek teeth (CT). STUDY DESIGN: Short case series. ANIMALS: Nine adult cattle. METHODS: Medical records were searched for cattle having a diagnosis of apical infection of CT that were treated with surgical extraction between 2005 and 2017. Data retrieved included clinical examination, ancillary tests, surgical procedure, and outcomes. RESULTS: The main presenting complaints were mandibular swelling and decreased appetite and milk production. In total, 7 mandibular and 3 maxillary CT were extracted, 7 molars and 3 premolars that were distributed more frequently on the left dental arcades (n = 7 CT). Two cattle had no visible external lesions. Radiograph images revealed that lucency surrounded all affected tooth roots. Mandibular teeth were removed by lateral buccotomy with removal of alveolar bone plate or retrograde repulsion, and maxillary teeth were removed by repulsion through a maxillary sinus flap. Most common bacterial isolates consisted of anaerobic bacteria (6/11 isolates) and Truperella pyogenes (3/11 isolates). The most common complications included inability to remove the tooth intact (n = 4 cattle) and surgical site infection (n = 5). All cattle remained in their herd after treatment. CONCLUSION: Surgical extraction of CT was achieved in all 9 cattle. The postoperative morbidity was high but without long-term consequences on animal productivity. CLINICAL SIGNIFICANCE: Surgical extraction of CT is a successful treatment for apical infection in cattle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".