Erythroid side scatter: A parameter that improves diagnostic accuracy of flow cytometry myelodysplastic syndrome scoring
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Flow cytometry immunophenotyping (FCM) is a benchmark test for integrated diagnosis of myelodysplastic syndromes (MDS). Our department's FCM-MDS-score follows international guidelines and additionally includes the maturing erythroid (mEry) side scatter (SSC)/lymphocyte SSC ratio (mErySSCr), often increased in MDS patients. A recent exploratory computational flow analysis study highlighted mErySSC as the top feature for separating MDS from non-MDS. Thus, we sought to systematically evaluate the diagnostic accuracy of mErySSCr in conventional diagnostic FCM as used currently in-house. METHODS: Historical MDS (n = 93), chronic myelomonocytic leukemia (CMML; n = 27) and non-neoplastic cytopenia (n = 57) cohorts were created. Differences between these cohorts and LG-MDS entities were mapped and the mErySSCr cut-off was refined. Prospective bone marrows (n = 213) received for marrow failure work-up were used to determine the sensitivity and specificity of mErySSCr, both as a sole parameter and as a component of the MDS-score. RESULTS: Low-grade (LG)-MDS mErySSCr differed more prominently from controls (p = <0.0001) than high-grade (HG)-MDS (p = 0.024). CMML and controls had a similar mErySSCr. As sole parameter, mErySSCr specificity was 91.1% (n = 112 non-MDS diagnoses) and sensitivity was 36% for LG-MDS (n = 36) and 25% for new HG-MDS diagnoses (n = 16). The specificity of the MDS-score was similar if mErySSCr was omitted (81.3% with and 82.1% without). The MDS-score sensitivity for new HG-MDS diagnoses and CMML (n = 17) was 100%, and was not affected by mErySSCr. The score sensitivity for LG-MDS however, dropped from 86.1% to 72.2% when mErySSCr was excluded. CONCLUSION: mErySSCr increases the diagnostic accuracy of flow-based MDS scoring in our setting, particularly for LG-MDS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it