Multi-Center Evaluation of the Automated Immunohematology Instrument, the ORTHO VISION Analyzer
Bibliographic record
Abstract
BACKGROUND: ORTHO VISION Analyzer (Vision), is an immunohematology instrument using ID-MT gel card technology with digital image processing. It has a continuous, random sample access with STAT priority processing. The efficiency and ease of operation of Vision was evaluated at 5 medical centers. METHODS: De-identified patient samples were tested on the ORTHO ProVue Analyzer (ProVue) and repeated on the Vision mimicking the daily workload pattern. Turnaround times (TAT) were collected and compared. Operators rated key features of the analyzer on a scale of 1 to 5. RESULTS: A total of 507 samples were tested on both instruments at the 5 trial sites. The mean TAT (SD) were 31.6 minutes (5.5) with Vision and 35.7 minutes (8.4) with ProVue, which renders a 12% reduction. Type and screens were performed on 381 samples; the mean TAT (SD) was 32.2 minutes (4.5) with Vision and 37.0 minutes (7.4) with ProVue. Antibody identification with eleven panel cells was performed on 134 samples on Vision; TAT (SD) was 43.2 minutes (8.3). The installation, training, configuration, maintenance and validation processes are all streamlined to provide a short implementation time. The average rating of main functions by the operators was 4.1 to 4.8. Opportunities for improvement, such as flexibility with editing QC results, maintenance schedule, and printing options were identified. The capabilities to perform serial dilutions, to accept pediatric tubes, and review results by e-Connectivity are enhancements over the ProVue. CONCLUSIONS: Vision provides shorter TAT compared to ProVue. Every site described a positive experience using Vision.
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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 | 0.012 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".