Direct Screening of Clinical Specimens for Multiple Respiratory Pathogens Using the Genaco Respiratory Panels 1 and 2
Bibliographic record
Abstract
We report here on the results of a pilot study comparing our clinical diagnostic virology laboratory's current methods of respiratory pathogen detection against the Genaco Respiratory Infections Panels 1 and 2. These assays employ xMap (Luminex) liquid phase bead conjugated array technology to facilitate automated detection of PCR and RT-PCR products, which provides potential for levels of assay multiplexing above those currently practical with either conventional gel-resolved or real-time methods. In the study presented here we used the Genaco panels to simultaneously screen previously analyzed clinical specimens (nasopharyngeal washings) for twenty-one important pathogens. Our results indicate the Genaco panels met or exceeded our current methods' sensitivity and specificity although allowing for detection of a wider range of infectious agents than practical by current diagnostic laboratory practices. In addition, the Genaco panels provided data on the presence of multiple respiratory pathogens in single specimens, which would otherwise be missed in most instances. To our knowledge, this study represents the first trial of these panels on standard clinical specimens in a routine diagnostic setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".