Assessing changes to the fecal microbiota in dogs undergoing elective orthopedic surgery: A preliminary investigation
Bibliographic record
Abstract
BACKGROUND: Studies assessing the impact of surgery on the canine gut microbiota are limited. This study assessed the fecal microbiota before and up to 3 months after elective orthopedic surgery. METHODS: Fourteen client-owned dogs >1 year of age undergoing elective orthopedic surgery were recruited. Dogs received perioperative antibiotics only (perioperative cefazolin, n = 7) or were discharged with oral cephalexin following surgery for 5-7 days (n = 7) in conjunction with perioperative antibiotics. Fecal samples were collected at baseline and at recheck 1 (13-50 days post-operatively) and recheck 2 (55-90 days post-operatively). The fecal microbiota was analyzed using 16S amplicon sequencing. Alpha diversity was assessed with the Sobs Index, Shannon Diversity Index, and Inverse Simpson Index, whereas beta diversity was assessed with the Bray-Curtis Index and Jaccard Index. RESULTS: In the perioperative and post-operative antibiotic groups, the Inverse Simpson and Shannon Diversity Index differed between baseline and recheck 1 (p < 0.05), baseline and recheck 2 (p < 0.05), but not between recheck 1 and recheck 2 (p > 0.05). The Sobs Index was only significantly different between baseline and recheck 1 (p = 0.02) in both groups. The Bray-Curtis and Jaccard Index were significantly different at rechecks 1 and 2 compared to baseline (p > 0.05) in the post-operative antibiotic group but not in dogs that received only perioperative antibiotics. Both the Bray Curtis and Jaccard Index were significantly different between the antibiotic prescription types (p = 0.001) although measures of alpha diversity were not (p > 0.05). CONCLUSIONS: Alterations in community structure, richness, and diversity were identified in dogs undergoing elective orthopedic surgery, with many changes persisting at least 2-3 months post-operatively in dogs receiving perioperative and/or post-operative antibiotics.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".