Bacterial isolates of indolent ulcers in 43 dogs
Bibliographic record
Abstract
PURPOSE: To determine whether bacteria are isolated from canine indolent ulcers and evaluate their influence on clinical outcomes. METHODS: Swabs for anaerobic and aerobic culture were collected from indolent ulcers of 43 client-owned dogs presenting to the University of Saskatchewan Veterinary Medical Centre. Outcomes were compared between ulcers with bacterial isolates and those without. Medical therapy was reviewed. RESULTS: Bacteria were isolated in 8/43 ulcers: Three cultured two isolates and five cultured single isolates. Staphylococcus was the most common genus isolated and was present in six ulcers: Species included unspecified [2], pseudintermedius [2], schleiferi [1], and hominis [1]. Streptococcus was the second most common isolate present in two ulcers: Species included canis and agalactiae. Tobramycin was the most common antibiotic used in ulcers with bacterial isolates prior to referral (n = 3). One case did not have conclusive follow-up data from the referring veterinarian and was therefore excluded from further analysis. All seven culture-positive ulcers were recorded as healed without complication: six healing after one procedure and one healing after two procedures. Thirty-five ulcers were culture-negative. There was no difference in outcome between indolent ulcers with bacterial isolates and those with negative cultures (P = .7475). CONCLUSIONS: Bacteria were isolated from 19% of indolent ulcers, and Staphylococcus was the most common isolate. Bacterial isolation did not influence outcome.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".