Perinuclear Antineutrophil Cytoplasmic Autoantibodies and Anti-<i>Saccharomyces Cerevisiae</i>Antibodies as Serological Markers Are Not Specific in the Identification of Crohn’s Disease and Ulcerative Colitis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the diagnostic accuracy of perinuclear antineutrophil cytoplasmic autoantibodies (pANCAs) and anti-Saccharomyces cerevisiae antibodies (ASCAs), as single agents and in combination, for the diagnosis of Crohn's disease (CD) and ulcerative colitis (UC), including in cases of indeterminate colitis (IC). METHODS: The sera from a total of 98 patients were studied: 77 with CD, 16 with UC and five with IC. The medical records of these patients were reviewed for disease diagnosis, demographic data, and patient symptoms and medications. ELISAs were utilized to detect the presence of ASCAs and deoxyribonuclease-sensitive pANCAs, and these results were then compared with the patients' clinical data. RESULTS: For UC, a positive pANCA test alone provided a sensitivity of 50% and a specificity of 82%. For CD, a positive ASCA test alone provided a sensitivity of 40% and a specificity of 100%. A combination of pANCA-positive and ASCA-negative results showed a sensitivity of 50% and specificity of 90% for the diagnosis of UC. Similarly, the combination of ASCA-positive and pANCA-negative results provided a sensitivity and specificity of 32% and 100% for the diagnosis of CD, respectively. Interestingly, 80% of IC patients showed serology results consistent with UC. CONCLUSIONS: Although this combination of serological markers provides a diagnostic tool with generally high specificities, the low sensitivities of these serological markers, most notably in terms of CD, preclude the possibility that they can replace the tools currently used for inflammatory bowel disease diagnosis and management. It is possible, however, that these serological markers may prove beneficial in the management of IC.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".