Erroneous automated optical platelet counts in 1‐hour post‐transfusion blood samples
Bibliographic record
Abstract
Thrombocytopenic patients with acute leukemia may show high post-transfusion count increments that significantly exceed the number of transfused platelets. This study demonstrates that the automated hematology analyzer Sysmex XE-2100 reports erroneously high optical platelet counts when the blood sample contains particles in the size range of platelets or smaller. Thrombocytopenic or low-normal whole blood samples were spiked with 1 mum latex beads (n = 14) to mimic contaminants under controlled conditions. Optical and impedance measurements of spiked and control samples with the Sysmex XE-2100 were compared with the Advia 120 and the manual counts. The added beads unexpectedly increased the automated optical platelet counts in the Sysmex XE-2100 and, to a lesser extent, the Advia 120 (Wilcoxon signed ranks test, P < 0.05), while the beads were not included in the impedance or the manual microscopic platelet counts. Differential interference contrast microscopy was used to investigate samples from platelet concentrates for transfusion. Platelet concentrates (32/128) were identified as possible sources for particles that were microscopically distinct from platelets but would be included in the automated optical count. Transfusion of platelet concentrates containing contaminating particles can lead to unexpectedly high post-transfusion platelet counts and misdiagnosis of thrombocytopenic patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".