Serum Macrophage Migration Inhibitory Factor Levels in the Patients with Active Ulcerative Colitis
Bibliographic record
Abstract
Objective: Macrophage migration inhibitory factor (MIF) is a cytokine that plays a critical role in immunity and inflammation.We compared serum MIF levels in patients diagnosed with active ulcerative colitis (UC) for the 1 time with those in healthy controls and attempted to determine whether serum MIF levels were different in the patients with active UC. Materials and Methods:A total of 38 naive patients who were admitted to our hospital between 2019 and 2020 and diagnosed with active UC by colonoscopy were included in the study as the case group, and 37 patients without acute or chronic diseases whose colonoscopy was normal were included as the control group.Results: There was no statistically significant difference in MIF levels between the patients with UC and the control group (p>0.05).Serum MIF levels were analyzed by comparing the patients with UC and the control group in terms of disease localization and severity.The serum MIF levels of the patients with UC were grouped according to the Montreal classification (p>0.05) and the Truelove and Witts criteria.There was no statistically significant difference in serum MIF levels between the patient groups or between them and the control group (p>0.05).Conclusion: Serum MIF levels were not higher in the patients with naive active US than in healthy control subjects.There are not many previous clinical studies on this topic.Further clinical studies are needed to investigate serum MIF levels in UC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".