Performance Evaluation of the Plateletworks<sup>®</sup> in the Measurement of Blood Cell Counts as compared to the Beckman Coulter Unicel DXH 800
Bibliographic record
Abstract
Prior to undergoing cardiac surgery many patients may have impaired platelet function due to platelet inhibition. Point of care testing (POCT) that produces quick results of platelet counts and function allow earlier clinician interpretation, diagnosis and treatment. Before being adopted for routine clinical use, a POCT device's performance must be evaluated by standard laboratory techniques to ensure high quality results. The purpose of this study is to determine the performance of the Plateletworks?V BC 3200 automated hematology analyzer by correlating its precision, accuracy and linearity for the measurement of blood counts to our hospital central laboratory analyzer (Beckman Coulter Unicel DXH 800). The study utilizes well described methods for Within-Run and Day-to-Day precision, comparison of methods (bias), and linearity. Control samples from the manufacturer were used for the precision studies, blood samples from 115 cardiac surgical subjects were used for comparison of methods and accuracy, and pre-diluted control samples from the manufacturer were used for the linearity studies. The precision of the Plateletworks® analyzer was acceptable. The overall coefficient of variation (CV) for the measured parameters at all levels of control for Within-Run precision was acceptable ranging from 0.65-6.4%. Likewise, the CV for the measured parameters at all levels of control for Day-to-Day precision was acceptable ranging from 1.45% to 6.7%. The correlation and accuracy between the two analyzers for the evaluated parameters (platelets, red blood cells, white blood cells, and hemoglobin) was acceptable. The linearity for the measured parameters was also acceptable with a range between 98-100%. The performance of the Plateletworks® analyzer was acceptable for providing blood cell counts as compared to our central hospital laboratory analyzer.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".