Workflow and Maintenance Characteristics of Five Automated Laboratory Instruments for the Diagnosis of Sexually Transmitted Infections
Bibliographic record
Abstract
The choice of a suitable automated system for a diagnostic laboratory depends on various factors. Comparative workflow studies provide quantifiable and objective metrics to determine hands-on time during specimen handling and processing, reagent preparation, return visits and maintenance, and test turnaround time and throughput. Using objective time study techniques, workflow characteristics for processing 96 and 192 tests were determined on m2000 RealTime (Abbott Molecular), Viper XTR (Becton Dickinson), cobas 4800 (Roche Molecular Diagnostics), Tigris (Hologic Gen-Probe), and Panther (Hologic Gen-Probe) platforms using second-generation assays for Chlamydia trachomatis and Neisseria gonorrhoeae. A combination of operational and maintenance steps requiring manual labor showed that Panther had the shortest overall hands-on times and Viper XTR the longest. Both Panther and Tigris showed greater efficiency whether 96 or 192 tests were processed. Viper XTR and Panther had the shortest times to results and m2000 RealTime the longest. Sample preparation and loading time was the shortest for Panther and longest for cobas 4800. Mandatory return visits were required only for m2000 RealTime and cobas 4800 when 96 tests were processed, and both required substantially more hands-on time than the other systems due to increased numbers of return visits when 192 tests were processed. These results show that there are substantial differences in the amount of labor required to operate each system. Assay performance, instrumentation, testing capacity, workflow, maintenance, and reagent costs should be considered in choosing a system.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".