Non tuberculous mycobacterial pulmonary disease (NTM-PD) and coinfections: a single center analysis.
Bibliographic record
Abstract
Introduction. NTM-PD presents a significant challenge to treat. Coinfections may complicate treatment worsening respiratory symptoms. Aim. To evaluate patients’ respiratory symptoms after treatment of bacterial or fungal coinfections, and examine potential predisposing factors that may increase the risk of developing simultaneous infections. Methods. We analysed 54 patients (41 females, 13 males) with isolation of NTM, mostly with bronchiectasis, from the IRCCS San Matteo Respiratory Unit. 47 NTM isolations with coinfections were assessed. Additionally, to identify potential predictive factors for coinfection, we divided 41 patients into two groups: those with NTM infection only and those with both NTM and simultaneous fungal infection (25 vs 16 patients respectively). Results. For coinfection, antibiotics were started in 23 out of 29 patients and antifungals in 10 out of 18. Improvement in respiratory symptoms was observed in 19 out of 23 treated with antibiotics (chi-squared = 9.6678, p-value = 0.001875) and in 9 out of 10 cases treated with antifungals (chi-squared = 10.8113, p-value = 0.001009), with a total of 28 cases (chi-squared = 23.0153, p-value < 0.00001). Regarding NTM infection and fungal coinfection, at diagnosis, patients had a higher FACED score and more bronchiectasis-affected lung lobes compared to those with isolated NTM (2.62 vs 1.8 and 3.54 vs 2.7, respectively). Discussion. Our analysis suggests that coinfection treatment may be a feasible option for improvement in symptoms and overall quality of life. A more severe radiological presentation and higher FACED score at diagnosis may increase the likelihood of coinfection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".