Validation and implementation of a new hemoglobinometer for donor screening at Canadian Blood Services
Bibliographic record
Abstract
BACKGROUND: Hemoglobin (Hgb) determination is an essential part of donor qualification. We assessed and implemented a new spectrophotometer for donor Hgb determination. STUDY DESIGN AND METHODS: Precision, accuracy, and ease of use were assessed on a prototype DiaSpect analyzer (DiaSpect Medical, GmBH, Sailauf, Germany). A protocol to qualify the analyzer was developed and executed preimplementation. Samples were developed for periodic quality control (QC). Postimplementation performance was assessed based on QC results and trending of deferral rates. RESULTS: Precision was excellent, with a coefficient of variation of 0.53%-1.14% per sample. The correlation coefficient between capillary DiaSpect and venous laboratory autoanalyzer measurements was 0.736. After 169 out of 223 analyzers failed to qualify on our initial protocol, all were successfully qualified with the use of a modified protocol, adjusted to avoid sources of variability. Because commercial controls proved inadequate, in-house samples were developed for periodic QC. Postimplementation, all analyzers had adequate QC results. Deferral rates decreased from 10.1 to 8.1% (p < 0.0001) for female donors and from 0.8 to 0.6% for male donors (p < 0.0001). The system was faster and easier to use compared with our previous two-step process. CONCLUSION: We successfully implemented a new spectrophotometer, which resulted in greater efficiency, improved ease of use, and decreased deferrals.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".