Trends in platelet distributions from 2008 to 2017: a survey of twelve national and regional blood collectors
Bibliographic record
Abstract
BACKGROUND: This multi-national study evaluated changes in platelet (PLT) unit distributions at 12 national or regional blood collectors over a 10-year period. METHODS: Data on the total number of PLT distributions, the collection method, that is apheresis vs whole blood-derived (WBD), the PLT unit characteristics and post-collection modifications were obtained from 12 national or regional blood collectors from 2008 through 2017. Individual WBD PLT units were converted to apheresis equivalent units (i.e. a dose of PLTs) by dividing by 4, the typical pool size; WBD units that were pooled before distribution were counted as a single dose. RESULTS: Overall at these 12 blood collectors, the total number of PLTs distributed in 2008 was 1 373 200, which rose by 10·2% to 1 513 803 in 2017. The Japanese Red Cross, which distributes only apheresis PLTs, had a 13·4% increase in the number of distributions between the years 2008 and 2017, while the other 11 blood collectors combined demonstrated a 6·8% increase in distributions between these two years. Between the years 2008 and 2017, the changes in the proportion of apheresis, platelet-rich plasma and buffy coat PLT distributions were -29·9%, -70·7% and 80·0%, respectively. CONCLUSION: The number of PLT distributions increased during the 10-year study period despite prophylactic PLT transfusion thresholds having remained fairly consistent over the last decade. Perhaps this increase is in part driven by increased administration of platelets to patients with massive haemorrhage or an increase in stem cell transplantation. The use of buffy coat PLTs is increasing at these collectors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".