Improving turn-around times in low-throughput distributed hematology laboratory settings with the CellaVision<sup>®</sup> DC-1 instrument
Bibliographic record
Abstract
Abstract Objectives Digital pathology is becoming standard in the delivery of timely, high-quality clinical services, inclusive of morphological assessment in laboratory hematology. While many digital hematology systems are designed with high-throughput in mind, CellaVision ® has recently developed a low-throughput instrument, the CellaVision ® DC-1. The utility of the CellaVision ® DC-1 was tested in a distributed laboratory system, with a focus on turn-around times (TATs). Methods We evaluated the TATs of a CellaVision ® DC-1 workflow, with specimens originating in a small spoke-laboratory referring materials to a central hub-laboratory. Our spoke-laboratories perform on-site complete blood counts (CBC’s) and manual peripheral blood smears (PBS’s), with complex cases referred for review to the hub-laboratory. Baseline TATs were collected, followed by prospective evaluation of 21 cases analyzed using the CellaVision ® DC-1, with digital review by spoke-laboratory staff in concert with remote review by hub-laboratory staff. The TATs for the same 21 cases by standard manual assessment were compared. Results Improvement in the distribution of TATs using the CellaVision ® DC-1 was noted relative to the retrospective spoke-laboratory data (Mann–Whitney U=26, p<0.0001) and the parallel manual PBS review (Wilcoxon W=190, p<0.0001). The CellaVision ® DC-1 permitted a significant reduction in case-assessment times (Wilcoxon W=105, p=0.0001). No significant diagnostic discrepancies were identified during the testing timeframe. Conclusions We describe a real-world assessment of the CellaVision ® DC-1 analyzer in a distributed (hub-and-spoke) laboratory network, linking low-volume laboratories to high-throughput sites. Our evaluation highlights significant improvements in case TATs with a CellaVision ® DC-1 assisted digital pathology workflow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".