Mind The Gaps
Bibliographic record
Abstract
OBJECTIVES: Confocal endomicroscopy can be used to image intestinal mucosa. Epithelial gaps resulting from shedding of epithelial cells have been reported in patients. We hypothesize that the rate of epithelial cell shedding increases in patients with Crohn's disease, leading to more epithelial gaps and barrier dysfunction. In this study, we used probe-based confocal laser endomicroscopy to quantify epithelial cells and gaps in patients with Crohn's disease compared with controls. We also determined the density of epithelial gaps in a mouse model of inflammatory bowel disease-interleukin-10-deficient (IL-10) mice, versus the background strain using rigid probe confocal endomicroscopy. METHODS: Probe-based confocal laser endomicroscopy of the terminal ileum of both patients with Crohn's disease and controls was performed by a single endoscopist during colonoscopy. In mice, sections of the small intestine were imaged using a rigid confocal probe. Gap density was defined as the number of epithelial gaps per 1000 cells counted. RESULTS: In this study, we examined 6 controls (2 male and 4 female; median age 59 y) and 8 patients with Crohn's disease (5 male and 3 female; median age 42 y). The mean gap densities (±standard error) observed for the 2 groups were 17.7±5.6 and 117±33 gaps per 1000 cells, respectively (P<0.01). For control and IL-10 mice, the gap densities were 10.5±2.2 and 17.8±1.4 gaps per 1000 cells, respectively (P<0.01). CONCLUSIONS: The epithelial gap density was significantly higher in patients with Crohn's disease than controls. Gap density was also elevated in the mouse model of inflammatory bowel disease.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".