Erythroid side scatter: A parameter that improves diagnostic accuracy of flow cytometry myelodysplastic syndrome scoring
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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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".