Immunohistochemistry and real-time Polymerase Chain Reaction: importance in the diagnosis of intestinal tuberculosis in a Peruvian population
Bibliographic record
Abstract
INTRODUCTION: The diagnosis of intestinal tuberculosis is challenging even nowadays. This study aims to report the positivity rates of new diagnostic methods such as immunohistochemistry and Real-Time Polymerase Chain Reaction in patients with intestinal tuberculosis, as well as describe the pathological and endoscopic features of intestinal tuberculosis in our population. METHODS: This was a retrospective observational study conducted in patients diagnosed with intestinal tuberculosis, between 2010 to 2023 from the Hospital Nacional Daniel Alcides Carrion and a Private Pathology Center, both located in Peru. Clinical data was obtained, histologic features were independently re-evaluated by three pathologists; and immunohistochemistry and real-time Polymerase Chain Reaction evaluation were performed. The 33 patients with intestinal tuberculosis who fulfilled the inclusion criteria were recruited. RESULTS: Immunohistochemistry was positive in 90.9% of cases, while real-time Polymerase Chain Reaction was positive in 38.7%. The ileocecal region was the most affected area (33.3%), and the most frequent endoscopic appearance was an ulcer (63.6%). Most of the granulomas were composed solely of epithelioid histiocytes (75.8%). Crypt architectural disarray was the second most frequent histologic finding (78.8%) after granulomas, but most of them were mild. CONCLUSION: Since immunohistochemistry does not require an intact cell wall, it demonstrates higher sensitivity compared to Ziehl-Neelsen staining. Therefore, it could be helpful for the diagnosis of paucibacillary tuberculosis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".