Longitudinal Changes in Fecal Microbiota During Hospitalization in Horses With Different Types of Colic
Bibliographic record
Abstract
BACKGROUND: Research on fecal microbiota changes during hospitalization of horses with colic is emerging. OBJECTIVES: Describe changes of the fecal microbiota during hospitalization of horses with colic caused by inflammatory (INFL), simple (SIMPLE), and strangulated (STR) obstructions, and investigate associations with survival. ANIMALS: Twenty-three horses with colic: 9 in INFL, 5 in STR, and 9 in SIMPLE groups. Seventeen horses survived, and 6 were euthanized. METHODS: Prospective observational study. Fecal samples were collected on admission (D1), on days 3 (D3) and 5 (D5). Bacterial taxonomy profiling was obtained by V1V3 16S amplicon sequencing. Data were compared using a 2-way permutational analysis of variance (PERMANOVA). Linear discriminant analysis Effect Size (LEfSE) analysis identified significant bacterial population differences, with significance set at p < 0.05 and a linear discriminant analysis (LDA) cut-off > 3.0. RESULTS: Alpha diversity indices remained stable during hospitalization within each colic group. However, at D5, the INFL group had significantly higher richness (p < 0.01) and diversity (Shannon, p < 0.001 and Simpson, p < 0.05) than other colic types. Beta diversity (Jaccard membership and Bray-Curtis indices) was significantly different in the INFL compared to SIMPLE and STR groups (both p < 0.001) but not between SIMPLE and STR. Beta diversity membership analysis by analysis of molecular variance (AMOVA) indicated a significant difference between survivors and non-survivors within the INFL group (p < 0.01). Increased relative abundances of Bacilliculturomica and Saccharofermentans were associated with survival. CONCLUSIONS: Microbiota showed no significant variation over 5 days of hospitalization. Colic type influenced fecal microbiota more than hospitalization duration. Specific bacterial populations may differ between survival and non-survival groups.
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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 | 0.000 |
| 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".