Clinical Evaluation of the ONETest, a Target-capture Next Generation Sequencing Platform for the Identification of Respiratory Pathogens
Bibliographic record
Abstract
We clinically evaluated the diagnostic capabilities of a target capture next generation sequencing (NGS) platform called the ONETestTM. The ONETest facilitates the simultaneous detection of respiratory pathogens through a comprehensive pipeline that includes specimen processing, NGS, and bioinformatic analysis. Clinical specimens (n=655) were collected from November 2019 to April 2020 at two hospitals in Toronto, Ontario, and another 251 specimens were collected in December 2020 from healthy community participants. All specimens were evaluated using the ONETest and conventional diagnostic techniques. Agreement statistics indicate a high level of agreement between the ONETest and conventional diagnostic techniques. The overall sensitivity, specificity, positive predictive value and negative predictive value of the ONETest was 90.9% [95% CI 87.8% - 93.4%], 97.6% [95% CI 97.1% - 98.0%], 77.7% [95% CI 73.9% - 81.2%], and 99.1% [95% CI 98.8% - 99.4%], respectively. These findings highlight the efficacy of the ONETest to detect respiratory pathogens accurately.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".