Enhanced identification of viral and atypical bacterial pathogens in lower respiratory tract samples with nucleic acid amplification tests
Bibliographic record
Abstract
The advantages of nucleic acid amplification tests (NAT) over conventional methods for the detection of pathogens in lower respiratory tract samples have not been established. NAT for respiratory pathogens were performed on 439 endotracheal tube (ETT) and bronchoalveolar lavage (BAL) samples. A potential pathogen was detected in 87 samples. Of 22 samples that tested positive by conventional methods, 15 tested positive for the same pathogen by NAT, 1 tested positive for a different pathogen, 2 had co-infections identified only by NAT, and 4 tested negative by NAT. An additional 73 pathogens were detected by NAT in 65 samples including 30 pathogens that were missed by conventional methods (19 adenovirus, 6 respiratory syncytial virus, 3 parainfluenza virus 1-4, 2 influenza A), 41 pathogens not routinely identified by conventional methods in most laboratories (23 rhinovirus, 8 human coronavirus OC43, 5 human metapneumovirus (hMPV), 2 human coronavirus 229E, 2 human coronavirus NL63, 1 Chlamydophila pneumoniae) and 2 pathogens from samples where no respiratory virus testing was requested (1 influenza A, 1 parainfluenza virus). Four of 52 patients who had multiple BAL samples submitted on the same day had negative and positive results by NAT on different samples. NAT improves detection of potential pathogens from ETT and BAL samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".