Report of the International Council for Standardization in Haematology working group for standardization of reticulocyte parameters
Bibliographic record
Abstract
INTRODUCTION: The International Council for Standardization in Haematology convened a working group to assess and propose improvements upon the state of standardization and harmonization of reticulocyte parameters among commercial hematology analyzers. METHODS: An international group of laboratory hematologists prospectively collected and analyzed clinical samples using locally available IVD commercial hematology analyzers. Eight hundred and fifty-five total samples were collected at 6 sites using 9 distinct analyzer types. Samples were assessed for reticulocyte percent (RET%), immature reticulocyte fraction (IRF), and reticulocyte hemoglobin content (RHC). Method comparison and regression statistics were calculated. These analyses were used to determine whether statistical recalibration offered a potential avenue for increasing comparability between these methods. RESULTS: While methods producing reticulocyte percent were the most comparable in this study, the state of harmonization for the IRF and RHC was reduced with pearson correlation coefficients ranging from 0.955 to 0.77 and 0.927 and 0.680, respectively. Nevertheless, use of parameters from the Passing Bablok regression substantially improved the comparability of the results. In addition, precision data was derived which also demonstrated substantial differences between analyzer systems. CONCLUSION: While reticulocyte counting is correlated between the automated methods evaluated in this study, the current state of harmonization of other reticulocyte parameters is not as strong. A major challenge in moving this field forward is the need for commutable materials to facilitate comparisons between analyzers not co-located. A potential alternate approach to improve the current state would be instrument re-calibration. However, this is challenging both technically and due to national regulatory frameworks.
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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 | 0.230 | 0.171 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".