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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".