Sinobronchial allergic mycosis syndrome in an elderly male
Bibliographic record
Abstract
Allergic bronchopulmonary aspergillosis (ABPA) and allergic fungal rhinosinusitis (AFRS) are characterized by hyper-responsiveness of the respiratory tract and the nasal cavity and paranasal sinuses, respectively to Aspergillus species and AFRS causes chronic rhinosinusitis. Herein, we report the first case of sinobronchial allergic mycosis (SAM) syndrome, defined as ABPA with concomitant AFRS, caused by Aspergillus fumigatus patient > 80 years. An 82-year-old male with interstitial pneumonia who returned for follow-up exhibited high-attenuation mucus plug in the right intermediate bronchial trunk, infiltration in the right lung field, and right pleural effusion on regular chest computed tomography (CT). We found unilateral central bronchiectasis in the right upper lobe. Similarly, CT scan of the paranasal sinuses revealed high-attenuation mucus plugs in left ethmoid sinuses. Biopsy specimens from the plugs in the right intermediate bronchial trunk and the left ethmoid sinuses revealed allergic mucin with layers of mucus eosinophils, eosinophil-predominant mixed inflammatory cell infiltrate and Aspergillus hyphae. The patient fulfilled all the major criteria for ABPA and AFRS, and was diagnosed with SAM syndrome. CT scan of the lung and paranasal sinuses revealed apparent amelioration after oral steroid therapy. Despite mostly reported in relatively young patients, SAM syndrome can occur in elderly individuals as well.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".