Comprehensive Detection of Candidate Pathogens in the Lower Respiratory Tract of Pediatric Patients With Unexpected Cardiopulmonary Deterioration Using Next-Generation Sequencing
Bibliographic record
Abstract
OBJECTIVES: Next-generation sequencing has been applied to the investigation of microorganisms in several clinical settings. We investigated the infectious etiologies in respiratory specimens from pediatric patients with unexpected cardiopulmonary deterioration using next-generation sequencing. DESIGN: Retrospective, single-center, observational study. SETTING: Tertiary care, a children's hospital. SUBJECTS: The study enrolled a total of 16 pediatric patients with unexpected cardiopulmonary deterioration who were admitted to the PICU. Ten bronchoalveolar lavage fluid and six transtracheal aspirate samples were analyzed. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: RNA libraries were prepared from specimens and analyzed using next-generation sequencing. One or more bacterial/viral pathogens were detected in the bronchoalveolar lavage fluid or transtracheal aspirate specimens from 10 patients. Bacterial and viral coinfection was considered in four cases. Compared with the conventional culture and viral antigen test results, an additional six bacterial and four viral pathogens were identified by next-generation sequencing. Conversely, among 18 pathogens identified by the conventional methods, nine pathogens were detected by next-generation sequencing. Candidate pathogens (e.g., coxsackievirus A6 and Chlamydia trachomatis) were detected by next-generation sequencing in four of 10 patients in whom no causative pathogen had been identified by conventional methods. CONCLUSIONS: Our results suggest that viral and bacterial infections are common triggers in unexpected cardiopulmonary deterioration in pediatric patients. Next-generation sequencing has the potential to contribute to clarification of the etiology of pediatric critical illness.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".