Improved harmonization of eosin‐5‐maleimide binding test across different instruments and age groups
Bibliographic record
Abstract
BACKGROUND: The eosin-5'maleimide (EMA) binding test has been studied extensively for the detection of hereditary spherocytosis (HS). Its performance characteristics have been compared to NaCl-based or glycerol lysis-based red cell osmotic fragility tests and cryohemolysis. HS samples are also better identified when both mean channel fluorescence (MCF) of EMA relative to controls and the coefficient of variation (CV) are analyzed. METHODS: We looked at 65 normal controls including 30 adults 25-65 years old and 35 newborns and 12 HS cases. In addition to the MCF and the CV, we used a side scatter (SSC) vs. EMA fluorescence gate or "footprint" to depict where normal erythrocytes should appear. Erythrocytes that have reduced band 3 protein appear outside of the footprint. RESULTS: In our study, newborn data did not cluster with the samples from working age individuals. The MCF and the CVs of normal newborns were higher than normal adult group. However, the footprint data of normal samples relative to their controls was around 99.5% for each group, because the footprint was moved to fit the pattern of the normal. CONCLUSIONS: The inclusion of footprint parameter will help in better standardization as well as implementation of this test across different age groups as well as different instruments. © 2015 International Clinical Cytometry Society.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".