Counting platelets at transfusion threshold levels: impact on the decision to transfuse. A BEST Collaborative ‐ UK NEQAS(H) International Exercise
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Obtaining accurate and precise platelet enumeration in automatic platelet analysers at low platelet counts is a challenge. To explore the performance of current haematology analysers in counting platelet concentrations usually used as platelet transfusion threshold. MATERIAL AND METHODS: An international exercise where four blood samples with platelet levels near usual platelet transfusion thresholds was prepared and distributed. RESULTS: The samples shipped had a platelet count of 6·3, 13·3, 21·6 and 53·0 × 10(9) /l according to the international reference method. We received 82 sets of results from nine countries. Instruments from six different manufacturers were represented. Although the mean count for each of the four samples was very similar to the values, according to the reference method (9·0, 16·2, 23·0 and 57·6 × 10(9) /l), significant variability in the results was found. Assuming that these were patient samples and the result of the count used to indicate a prophylactic platelet transfusion, undertransfusion would have occurred for 24·5% of the LP1 samples at a transfusion threshold of 10 × 10(9) /l and, at a threshold of 20 × 10(9) /l, undertransfusion would have occurred for 7·2% of the LP1 and 16·2% of the LP2 samples and overtransfusion would have occurred with 23·1% of the LP3 samples. CONCLUSION: The results suggest that significant inaccuracy exists in counting low levels of platelets and that this inaccuracy might have a significant impact in under- and overtransfusion of platelet concentrates to patients.
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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 | 0.041 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".