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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".