PCR detection of <i>Mycobacterium tuberculosis</i> in necrotising non-granulomatous lymphadenitis using formalin-fixed paraffin-embedded tissue: a study in Thai patients
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Necrotising non-granulomatous lymphadenitis can be observed in several conditions, most notably infection (including tuberculosis, yersiniosis and nocardiasis), Kikuchi-Fujimoto disease and systemic lupus erythematosus. AIMS: To evaluate the role of PCR in the detection of Mycobacterium tuberculosis in necrotising non-granulomatous lymphadenitis in Thai patients using formalin-fixed paraffin-embedded tissue. METHODS: 35 patient samples showing necrotising non-granulomatous lymphadenitis were subjected to PCR for detection of the IS6110 sequence of M tuberculosis. For comparison, sections were visually assessed for acid-fast bacilli using the Ziehl-Neelsen stain. RESULTS: Among 35 cases of necrotising non-granulomatous lymphadenitis, a conclusive diagnosis could be reached in 23 cases: 15 cases of Kikuchi-Fujimoto disease, 6 of tuberculosis and 2 of systemic lupus erythematosus. Of the 6 cases of tuberculous lymphadenitis, 4 (66.6%) were detected by PCR in formalin-fixed paraffin-embedded tissue samples. PCR was positive in 6/12 of the remaining cases (50%) in which a definitive diagnosis could not be reached by other methods. CONCLUSION: Using PCR, a significant percentage (28%) of cases of necrotising non-granulomatous lymphadenitis in this study could be attributed to M tuberculosis. PCR for identification of the organism can be extremely helpful in confirming a diagnosis of tuberculosis when Ziehl-Neelsen staining is negative.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it