Recent Trend of Laboratory Tests in Common Gastrointestinal Tract Disorders
Bibliographic record
Abstract
infection, inflammatory bowel disease (IBD), celiac disease, irritable bowel syndrome (IBS), and colon cancer, are common and cause significant morbidity, mortality, and healthcare costs. These disorders present with overlapping signs and symptoms, warranting the need for accurate laboratory diagnostic tests for appropriate treatment implementation and treatment monitoring. The gold standard confirmatory diagnostic test for most GI disorders is endoscopy and biopsy for histological analysis. Biomarkers in blood and stool are also routinely used either as first-line screening tests or for treatment monitoring in many GI disorders. This review summarizes common GI disorders along with related currently used clinical laboratory tests in screening, diagnosis, and monitoring of these diseases, outlining the methodology, utilization, advantages, and limitations of these tests. We also highlight the effectiveness of each test as well as the professional recommendations and clinical guidelines for their use where available. Finally, we shed some light on potential future tests and biomarkers that aid in diagnosing GI disorders and how these biomarkers can be used in conjunction to complement the current tests. Some of the potential future biomarkers discussed include the differential expression of gut microbiota and their respective metabolites, as well as cytokines, as potential tests that can be used to diagnose diseases, distinguish between disease subtypes, predict disease severity and occurrence, and optimize treatment decisions. Comprehending the effectiveness of various methodologies for laboratory diagnosis of GI disorders is crucial for health care personnel, including clinical laboratory professionals and clinicians, regarding testing options, test utilization, and interpretations of results. Insights into future tests in GI diseases in the context of microbiomes, metabolites, and immune mediators based on advanced technology are also important in their appropriate clinical utilization.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".