A Case Series of Non-Tuberculous Mycobacterial Pulmonary Disease Masquerading as Malignancy From a Community-Based Hospital
Bibliographic record
Abstract
Non-tuberculous mycobacteria (NTM) are ubiquitous organisms in the environment that can potentially cause a range of pulmonary and extrapulmonary infections in humans. Epidemiological risk factors and the host’s immune status determine the susceptibility to various clinical syndromes caused by different NTM species. Non-tuberculous mycobacteria pulmonary disease (NTM-PD) is primarily reported in patients with underlying lung disease. These infections often pose a significant disease burden on affected patients as they are often chronic, difficult to treat, and necessitate long-term multi-drug therapy. Mycobacterium avium complex (MAC) is the most common causative pathogen of NTM-PD in the USA, followed by Mycobacterium kansasii ( M. kansasii ). Less common species in the USA include Mycobacterium xenopi ( M. xenopi ), Mycobacterium abscessus , and others, largely depending upon the geographic location and exposure to species-specific predisposing risks. In this case series, the authors report on three elderly patients with chronic lung diseases who had pulmonary NTM disease caused by M. xenopi and MAC. The patients were encountered in both inpatient and outpatient settings from a community-based hospital in the midwestern USA. The clinical and radiological features of NTM-PD masqueraded as malignancy and posed a diagnostic dilemma. The epidemiology, clinical and radiological features, diagnosis, and management of NTM-PD are reviewed in this report. J Med Cases. 2023;14(4):141-147 doi: https://doi.org/10.14740/jmc4098
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".