Chest computed tomography predicts microbiological burden and symptoms in pulmonary <i>Mycobacterium xenopi</i>
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The development of computed tomography (CT) findings usually precedes the diagnosis of pulmonary nontuberculous mycobacterial infection. The utility of specific CT scan features, although often available long before respiratory sample cultures, is not fully understood. We sought to assess associations among CT features, symptoms and microbiological disease criteria in pulmonary Mycobacterium xenopi isolation. METHODS: We reviewed 70 consecutive immunocompetent patients with pulmonary M. xenopi isolation and classified them according to the American Thoracic Society (ATS) diagnostic criteria for disease. 'Definite disease' patients (n = 16) met modified ATS criteria. 'Possible disease' patients (n = 10) met microbiological criteria, had abnormal CT scans, but data regarding symptoms were unavailable. 'No disease' patients (n = 44) had only one positive sputum culture, or were asymptomatic or had no relevant CT findings. Two radiologists, without knowledge of the clinical or microbiological information, independently reviewed the scans. RESULTS: The mean (standard deviation) age of all patients was 63 (16) years, and 39% were women. Patients with 'definite disease' usually had nodules (88%) and cavities (63%), but less often bronchiectasis (50%) and tree-in-bud (50%). Patients with 'possible' or 'no disease', respectively, had nodules (100% or 80%), bronchiectasis (40% or 18%) or tree-in-bud (40% or 11%). Cavitation (P ≤ 0.0001) and nodules ≥ 5 mm (P = 0.0002) were associated with fulfilled microbiological criteria for disease. Bronchiectasis (P = 0.02) and nodules <5 mm (P = 0.002) were associated with symptoms of infection. CONCLUSIONS: Among immunocompetent patients with pulmonary M. xenopi isolation, cavitation and large nodules predict fulfilling microbiological disease criteria, while bronchiectasis and small nodules predict symptoms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".