Evidence from multiplex molecular assays for complex multipathogen interactions in acute respiratory infections.
Bibliographic record
Abstract
While most diagnostic processes cease with the detection of the first relevant infectious agent, newer multiplexed molecular methods which provide simultaneous analysis of multiple agents may give a more accurate representation of the true pathogen spectrum in these samples. To examine this in the context of respiratory infections, acute-phase respiratory specimens submitted to our clinical diagnostic microbiology/virology laboratory for our routine VIRAP diagnosis protocol during the spring 2006 peak respiratory infection season were processed in parallel by analysis with Genaco (QiaPlex) ResPlex I and ResPlex II molecular diagnostic panels. A total of 1,742 specimens were examined for 21 relevant targets each. The resulting data reveal that multiple infections are frequent and provide evidence for complex interactions between different infectious agents. Statistically relevant association patterns (both positive and negative) were observed between particular pathogens. While some interactions we observed are substantiated by prior reports in the literature, several specific patterns do not appear to have been reported previously. In addition, we report preliminary clinical evidence which supports a hypothesis that these coinfections are medically relevant and that effective treatment for severe respiratory tract infections will increasingly require diagnosis of all involved pathogens, as opposed to single-pathogen reporting.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".