PCR detection of <i>Mycobacterium tuberculosis</i> in necrotising non-granulomatous lymphadenitis using formalin-fixed paraffin-embedded tissue: a study in Thai patients
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".