Platelet count estimation using the CellaVision DM96 system
Bibliographic record
Abstract
INTRODUCTION: Rapid and accurate determination of platelet count is an important factor in diagnostic medicine. Traditional microscopic methods are labor intensive with variable results and are highly dependent on the individual training. Recent developments in automated peripheral blood differentials using a computerized system have shown many advantages as a viable alternative. The purpose of this paper was to determine the reliability and accuracy of the CellaVision DM 96 system with regards to platelet counts. MATERIALS AND METHODS: One hundred twenty seven peripheral blood smears were analyzed for platelet count by manual microscopy, an automated hematology analyzer (Beckman Counter LH 780 or Unicel DXH 800 analyzers) and with the CellaVision DM96 system. Results were compared using the correlations and Bland-Altman plots. RESULTS: Platelet counts from the DM96 system showed an R(2) of 0.94 when compared to manual platelet estimates and an R(2) of 0.92 when compared to the automated hematology analyzer results. Bland-Altman plots did not show any systematic bias.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".