Quality control of apheresis platelets: a multicentre study to evaluate factors that can influence <scp>pH</scp> measurement
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Blood operators routinely monitor the pH of apheresis platelets as a marker of the so-called storage lesion, which can result from manufacturing problems. It is also suspected that some donor characteristics can increase the risk of poor platelet storage. To explore this hypothesis, we analysed a large, multinational data set of quality control (QC) pH test results on apheresis platelets. MATERIALS AND METHODS: For the period between September 2011 and August 2014, seven blood operators in Canada, the USA, the Netherlands, the United Kingdom, France and Australia provided pH QC test results and donor characteristics on a total of 21,671 apheresis platelets. RESULTS: Some variations in pH distribution between blood operators were in part explained by differences in collection, processing and testing methods. Younger age and female gender were significantly associated with a pH value below the 10th percentile. Among donors who had two or more pH measurements (n = 3672), there was a strong correlation between pH results (r = 0·726; P < 0·0001). CONCLUSION: The strong intradonor correlation of pH measurements and the association between donor characteristics and pH results suggest that donor factors play a role in the quality of platelets.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".