Prevalence and Pathological Characteristics of Septic Eye Affections in Dogs: A Clinical Study
Bibliographic record
Abstract
Aims: To study the prevalence and pathology associated with septic eye affections in dogs of Jabalpur region. Place and Duration of Study: Veterinary Clinical Complex (VCC), College of Veterinary Science and A.H., Jabalpur and in private pet clinics of Jabalpur for period of seven months spanning from April 2024 to October 2024. Methodology: A total of 992 dogs were screened for eye affections. Amongst these, clinically 153 dogs had various eye affections and were included in the study. Eye swabs were collected aseptically from dogs suspected of having eye infections for bacterial isolation, identification and antibiotic sensitivity test (AST). Bacterial cultures were also subjected for molecular confirmation of the bacterial species. Results: Septic eye affections were observed in 70 dogs, with a prevalence of 45.75%. Male dogs (71.43%) and young dogs (up to 3 years) (44.28%) were more prone, with the Labrador Retriever breed most commonly affected, followed by non-descript breeds. A significant decrease in hemoglobin, packed cell volume, total erythrocyte count, monocytes and platelets was noted. The highest frequency of septic eye affections was recorded as conjunctivitis (35.71%). The most commonly isolated bacteria from septic eye affections were Staphylococcus spp. (57.69%), followed by Escherichia coli (11.53%). All Staphylococcus spp. and E. coli isolates tested positive for the genus-specific 16SrRNA gene using species-specific primers in PCR. Gentamicin, Chloramphenicol Amikacin and Erythromycin exhibited the highest sensitivity against all gram-positive bacteria, whereas Amoxiclav, Tetracycline, and Norfloxacin showed the highest sensitivity against all gram-negative bacteria. Conclusion: Conjunctivitis (35.71%) was found to be the most common septic eye affection and Staphylococcus spp. was highest among all the bacteria isolated from septic eye affections.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".