Identification of pathogens causing invasive fungal rhinosinusitis in surgical biopsies using polymerase chain reaction
Bibliographic record
Abstract
BACKGROUND: Invasive fungal rhinosinusitis is associated with high morbidity and mortality. Rapid pathogen identification is mandatory, but fresh tissue is not always available. A polymerase chain reaction method was designed in order to detect fungi in formalin-fixed paraffin-embedded samples. This was applied to a retrospective series of tissue biopsies from Thai patients with invasive fungal rhinosinusitis. METHODS: Tissue blocks from 64 cases yielded adequate DNA. Three sequential polymerase chain reaction were performed: ZP3 (housekeeping gene) and panfungal polymerase chain reactions, and a differentiating polymerase chain reaction based on the 5.8s ribosomal RNA and internal transcribed spacer 2 regions. The polymerase chain reaction products were then sequenced. RESULTS: Polymerase chain reaction identified a fungal pathogen in 20 of 64 cases (31 per cent). Aspergillus species was the most common cause of invasive fungal rhinosinusitis (nine cases). Other causes included candida (n = 4), cladosporium (n = 4), mucor (n = 1), alternaria (n = 1) and dendryphiella (n = 1) species. CONCLUSION: Polymerase chain reaction can provide rapid identification of fungal pathogens in paraffin-embedded tissue, enabling prompt treatment of invasive fungal rhinosinusitis.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".