Profile of gut microbiota in patients with traumatic thoracic spinal cord injury and its clinical implications: a case-control study in a rehabilitation setting
Bibliographic record
Abstract
Gut microbiota are the candidate biomarkers for neurogenic bowel dysfunction (NBD) in patients with spinal cord injury (SCI). We aimed to identify the common features between patients with varying degree of thoracic SCI and healthy individuals and subpopulations of microbiota correlated with the serum biomarkers. Twenty-one patients with complete thoracic SCI (CTSCI), 24 with incomplete thoracic SCI (ITSCI), and 24 healthy individuals (HC) were enrolled in this study. Fresh stool samples and clinical data were collected from all participants, and their bowel functions with SCI were assessed. Microbial diversity and composition were analyzed by sequencing the 16S rRNA gene. The features of gut microbiota correlated with the serum biomarkers and their functions were investigated. The mean NBD score of patients with CTSCI was higher than that of patients with ITSCI. Diversity of the gut microbiota in SCI group was reduced, and with an increase in the degree of damage, alpha diversity had decreased gradually. The composition of gut microbiota in patients with SCI was distinct from that in healthy individuals, and CTSCI group exhibited further deviation than ITSCI group compared to healthy individuals. Four serum biomarkers were found to be correlated with most differential genera. Patients with thoracic SCI present gut dysbiosis, which is more pronounced in patients with CTSCI than in those with ITSCI. Therefore, the gut microbiota profile may serve as the signatures for bowel and motor functions in patients with thoracic SCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".