Three-dimensional Computed Tomography Scan Whiskering in Ankylosing Spondylitis: A View from Inside
Bibliographic record
Abstract
ABSTRACT We compared the ability of 2 commercial molecular amplification assays [RealTime SARS-CoV-2 on the m2000 (Abbott) and ID NOW™ COVID-19 (Abbott)] and a laboratory-developed test [modified CDC 2019-nCoV RT-PCR assay with RNA extraction by eMag® (bioMérieux) and amplification on QuantStudio™ 6 or ABI 7500 Real-Time PCR System (Life Technologies)] to detect SARS-CoV-2 RNA in upper respiratory tract specimens. Discrepant results were adjudicated by medical record review. 200 nasopharyngeal swab specimens in viral transport medium (VTM) were collected from symptomatic patients between March 27 and April 9, 2020. Results were concordant for 167 specimens (83.5% overall agreement), including 94 positive and 73 negative specimens. The RealTime SARS-CoV-2 assay on the m2000 yielded 33 additional positive results, 25 of which were also positive by the modified CDC assay but not by the ID NOW™ COVID-19 assay. In a follow-up evaluation, 97 patients for whom a dry nasal swab specimen yielded negative results by the ID NOW™ COVID-19 assay had a paired nasopharyngeal swab specimen collected in VTM and tested by the RealTime SARS-CoV-2 assay; SARS-CoV-2 RNA was detected in 13 (13.4%) of these specimens. Medical record review deemed all discrepant results to be true positives. The ID NOW™ COVID-19 test was the easiest to perform and provided a result in the shortest time: as soon as 5 minutes for positive and 13 minutes for negative result. The RealTime SARS-CoV-2 assay on the m2000 detected more cases of COVID-19 infection than the modified CDC assay or the ID NOW™ COVID-19 test.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".