Comparison of cobas 4800, m2000, Viper XTR, and Infinity 80 Automated Instruments When Processing Urine Specimens for the Diagnosis of Chlamydia trachomatis and Neisseria gonorrhoeae
Bibliographic record
Abstract
OBJECTIVES: North American and European advisory groups recommend testing for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) with nucleic acid amplification tests. Testing is often performed on automated instruments. The objectives of this study were to process urines for the diagnosis of CT and NG and to examine workflow procedures and outcomes. METHODS: While processing 1, 24, 48, 96, and 192 urine specimens on 3 batch-mode systems which use 96-well plates: cobas 4800, m2000, and Viper XTR and the random access cartridge testing GeneXpert Infinity 80, we measured assay performance, hands-on time for processing and maintenance, reagents and plastics consumption, time required to obtain results, and testing accuracy. RESULTS: The Infinity 80 required the least hands-on time for single specimens and smaller batches, whereas the Viper XTR and m2000 required the most hands-on time for all batch sizes. Cumulative daily, weekly, and monthly maintenance was highest for the Viper XTR and lowest for Infinity 80. All batch-mode instruments consumed large amounts of disposables. Time to results was shortest for the Infinity 80, and the Viper XTR provided the shortest time for the batch-mode instruments. All systems showed similar diagnostic accuracy. CONCLUSIONS: Because detection performances were similar, issues of hands-on time, maintenance, time to results, and consumables are important operational factors for the diagnosis and treatment of CT/NG infections.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".