Validation of Plateletworks ADP for the ProCyte Dx analyzer
Bibliographic record
Abstract
BACKGROUND: Platelet function testing in cats allows determination of clopidogrel effect. Plateletworks assesses aggregation based on decreasing platelet counts on hematology analyzers in response to agonists. It has not been validated for the IDEXX ProCyte Dx analyzer. Ideal time to perform analysis and the utility of other platelet parameters have not been fully assessed. OBJECTIVES: To validate Plateletworks ADP on the ProCyte Dx, to investigate the utility of various platelet parameters using Plateletworks ADP, and determine the ideal time to perform analysis. ANIMALS: Twenty healthy cats recruited from the general population used for transference of reference intervals to a new analyzer, and 10 cats receiving clopidogrel to determine clopidogrel effect. METHODS: Plateletworks ADP using the ProCyte Dx and ADVIA 2120i analyzer was run simultaneously in both healthy cats and cats receiving clopidogrel, and CBC results at different timepoints were compared between analyzers. RESULTS: Aggregation was significantly different (P < .001) between analyzers. Cohen's kappa showed almost perfect agreement for determination of clopidogrel effect, and the area under the curve of the receiver operating characteristic was 1.0. Lower limits of the aggregation reference interval in healthy cats were 28.8% on the ProCyte Dx and 12.5% on the ADVIA 2120i. Coefficients of variation for platelet parameters were not different between analyzers. No significant changes in mean platelet volume, plateletcrit, large platelets, and mean platelet component were identified. No significant change in aggregation was observed within the first hour after phlebotomy. CONCLUSIONS AND CLINICAL IMPORTANCE: Our study validated the Plateletworks ADP system on the ProCyte Dx analyzer. Samples may be analyzed up to 1 h after collection.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".