Severe pneumonia caused by Legionella pneumophila associated with Tropheryma whipplei: A case report
Bibliographic record
Abstract
RATIONALE: Legionella pneumophila (LP) and Tropheryma whipplei (TW) have been identified as pathogens that can coexist in sewage environments, which can both cause pneumophila and can impact various organs, leading to extrapulmonary manifestations. It is rare to find literature on pneumonia caused by atypical pathogens, such as the combination of LP and TW. There are no reports on specific drug treatments for such combined infections. PATIENT CONCERNS: A 55-year-old male with no underlying diseases developed community-acquired pneumonia. His condition worsened after empirical treatment with piperacillin/tazobactam. He subsequently experienced adverse drug reactions after using moxifloxacin. DIAGNOSES: Pulmonary infection, pulmonary bullae. INTERVENTIONS: After admission, the patient was treated with piperacillin/tazobactam and moxifloxacin for anti-infection. He also underwent a bronchoscopy of the lungs, and the alveolar lavage fluid was tested using targeted next-generation sequencing. The results identified pathogens such as LP and TW. The anti-infection regimen was then adjusted to a combination of ceftriaxone and levofloxacin. Symptomatic treatments were also provided, including expectorant therapy, liver protection, and nutritional support. OUTCOMES: The patient was discharged after showing signs of improvement and was subsequently monitored at an outpatient clinic. LESSONS: When empirical antibiotic therapy for pneumonia is ineffective, infections caused by atypical pathogens should be considered. To prevent progression to severe pneumonia and even patient death, the precise use of antibiotics is beneficial for improving patient outcomes. However, the diagnosis of atypical pathogens is challenging, and the selection of drugs also needs to be considered from multiple aspects.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".