Computed tomographic abnormalities in Mycobacterium avium complex lung disease include the mosaic pattern of reduced lung attenuation.
Bibliographic record
Abstract
OBJECTIVE: To describe the range and frequency of abnormalities on computed tomographic (CT) scans in patients who met the American Thoracic Society (ATS) case definition of Mycobacterium avium complex (MAC) lung disease. Further, to report the effect of time and treatment on the progression of these abnormalities. METHODS: The demographic, clinical and radiologic features of 10 patients with proven MAC lung disease (mean age 71 years, standard deviation 12 years) were described. The presence or absence of 14 CT abnormalities was recorded in each of 10 lung zones. The effects of time and treatment on the abnormalities were recorded. RESULTS: The patients' lung function was minimally impaired. Pretreatment CT scans showed small nodules in 47% of the lung zones, reduced lung attenuation in 41%, and bronchiectasis in 27%. Bronchiectasis was associated with nodules in 20 zones from 9 patients and with no nodules in 7 zones from 1 patient. Reduced attenuation was associated with bronchiectasis alone in 8 zones, with nodules alone in 25 zones and with both bronchiectasis and nodules in 8 zones. In patients without treatment, or with non-curative treatment, bronchiectasis developed or worsened in 12 of 26 zones and resolved or improved in none. In contrast, after curative treatment, small nodules disappeared completely in 10 of 21 zones and developed in none. Of 7 zones with only nodules and reduced attenuation before treatment, 6 had no abnormalities after treatment. CONCLUSIONS: Our data suggest that the 3 leading CT abnormalities in nodular-bronchiectatic MAC lung disease are small nodules, reduced attenuation and bronchiectasis. Reduced attenuation appeared to result from gas trapping and hypoxic vasoconstriction due to bronchiolocentric inflammatory nodules. Bronchiectasis appeared to be secondary to this bronchiolar disease.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".