Management of nontuberculous mycobacteria in lung transplant cases: an international Delphi study
Bibliographic record
Abstract
Rationale Nontuberculous mycobacterial (NTM) diseases are difficult-to-treat infections, especially in lung transplant (LTx) candidates. Currently, there is a paucity of recommendations on the management of NTM infections in LTx, focusing onMycobacterium aviumcomplex (MAC),M. abscessusandM. kansasii. Methods Pulmonologists, infectious disease specialists, LTx surgeons and Delphi experts with expertise in NTM were recruited. A patient representative was also invited. Three questionnaires comprising questions with multiple response statements were distributed to panellists. Delphi methodology with a Likert scale of 11 points (5 to −5) was applied to define the agreement between experts. Responses from the first two questionnaires were collated to develop a final questionnaire. The consensus was described as a median rating >4 or <−4 indicating for or against the given statement. After the last round of questionnaires, a cumulative report was generated. Results Panellists recommend performing sputum cultures and a chest computed tomography scan for NTM screening in LTx candidates. Panellists recommend against absolute contraindication to LTx even with multiple positive sputum cultures for MAC,M. abscessusorM. kansasii.Panellists recommend MAC patients on antimicrobial treatment and culture negative can be listed for LTx without further delay. Panellists recommend 6 months of culture-negative forM. kansasii, but 12 months of further treatment from the time of culture-negative forM. abscessusbefore listing for LTx. Conclusion This NTM LTx study consensus statement provides essential recommendations for NTM management in LTx and can be utilised as an expert opinion while awaiting evidence-based contributions.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".