Streptococcus canis sequence type 43 may be associated with treatment failure in dogs with corneal ulceration
Bibliographic record
Abstract
OBJECTIVE: To profile Streptococcus canis isolates obtained from corneal ulcers in dogs. ANIMALS: 10 dogs. PROCEDURES: Medical records were searched to identify dogs diagnosed with ulcerative keratitis by a veterinary ophthalmologist and having a positive corneal culture for S canis during the year 2020. For each case, clinical findings and outcome were determined, antimicrobial resistance and sensitivity panels were summarized, whole genome sequencing was performed, and isolates were typed using multi-locus sequence typing and genome-based proteome phylogenetic analysis. RESULTS: 10 S canis isolates were included from dogs diagnosed with ulcerative keratitis. Dogs were either treated surgically via keratectomy and conjunctival grafting (n = 6) or treated medically (4). Three of 10 corneas failed to heal and required enucleation (2/6 conjunctival grafts and 1/4 medically managed corneal ulcers). All three corneal ulcers that failed to heal were associated with S canis sequence type (ST) 43. Sequence types identified from successfully treated cases included ST8 (n = 1), ST50 (1), ST2 (2), ST27 (1), and ST15 (1). One ST43 isolate was obtained from a dog that healed following a conjunctival graft, however this was the only dog that received an oral antibiotic in addition to topical antibiotics. CLINICAL RELEVANCE: Based on this small dataset, S canis ST43 may be associated with increased virulence and contribute to conjunctival graft failure and progressive corneal collagenolysis. The postoperative administration of an oral antimicrobial may protect against conjunctival graft rejection in dogs specifically due to S canis ST43.
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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 | 0.001 | 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".