Inflammatory Disorders of the Large Intestine
Bibliographic record
Abstract
Inflammation of the intestines is common worldwide. Infections are the major cause whereas chronic inflammatory bowel diseases (CIBDs), drugs and other conditions are less frequent causes. Worldwide, enteric infections rank third among all causes of disease burden. The main infectious agents include a variety of viruses and bacterial agents. As endoscopy and colonoscopy are used more frequently, the pathologist is more likely to see small and large intestinal biopsies for the diagnosis of inflammatory lesions, although histology most frequently does not contribute very much to the specific diagnosis of infections. Histology is, however, very important in chronic conditions, allowing the identification of pathogens, such as cytomegalovirus, and the differential diagnosis between CIBD and drug- or infection-related conditions, such as tuberculosis and acute infectious colitis, based on the morphology of the lesions. Histological analysis is equally important for the diagnosis of inflammation in patients with a disturbed immune system, such as transplant recipients, and for the identification of rare diseases, and the diagnosis of dysplasia and complicating neoplasias.
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
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".