Pulmonary Mycobacterium xenopi infection in non-HIV-infected patients: a systematic review.
Bibliographic record
Abstract
SETTING: The incidence of Mycobacterium xenopi infections is increasing worldwide. The characteristics and optimal management of patients with pulmonary M. xenopi infections have not been well established. METHODS: Systematic review of English- and French-language studies reporting at least two cases of microbiologically confirmed M. xenopi lung infection. Studies were independently reviewed by two reviewers. We described the risk factors and clinical presentation of advanced infection, and examined the impact on clinical success and mortality of including individual antimycobacterial drugs in the treatment regimen. RESULTS: A total of 48 studies reporting on 1255 subjects were included. The majority were retrospective case series. There was marked heterogeneity among the studies. Patients were generally middle-aged men with a history of obstructive lung disease or prior tuberculosis, presenting with an upper lobe cavitary infection. There was no clear association between administration of particular drugs and clinical success or mortality. CONCLUSION: We could not demonstrate any advantage of specific drugs in the treatment of pulmonary M.xenopi infection. Observations from the pooled data are likely subject to significant confounding and selection biases. The inability to make firm conclusions on the optimal management of this increasingly common infection strongly underscores the need for further research.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".