Combination of <scp>NeuX</scp> and <scp>NeuZ</scp> can predict neutrophil dysplasia features of <scp>myelodysplastic neoplasms</scp> in peripheral blood
Bibliographic record
Abstract
INTRODUCTION: The assessment of neutrophil dysplasia features in peripheral blood is very helpful for the early screening and diagnosis of myelodysplastic neoplasms (MDS). Cell population data (CPD) parameters generated by automated hematology analyzers can reflect morphological characteristics of blood cells. This study aimed to investigate the clinical significance of CPD parameters neutrophil (Neu) X, NeuY and NeuZ in assessing neutrophil dysplasia. METHODS: 218 MDS patients were divided into two subgroups according to neutrophil morphology. The differences of neutrophil research parameters between the two MDS subgroups and the control group, consisting of 210 healthy individuals, were compared, the correlation among neutrophil research parameters and the relationship between these parameters and cell morphology in MDS patients were analyzed, and receiver operating characteristic analysis were performed. RESULTS: The median values of neutrophil research parameters NeuX and NeuZ in MDS with granulocyte dysplasia group were significantly lower than those in MDS without granulocyte dysplasia group and control group (p < 0.001), and they were positively correlated (r = 0.878, p < 0.001). The area under the receiver operating characteristic curve of NeuX and NeuZ was 0.720 (95% CI: 0.643-0.796, p < 0.001) and 0.738 (95% CI: 0.665-0.811, p < 0.001), respectively. In addition, with the decrease of NeuX value, neutrophils gradually show decreased nuclear segment and/or cytoplasmic granules. CONCLUSIONS: Combining NeuX and NeuZ can predict neutrophil dysplasia features of MDS in peripheral blood, and this can be an easier method to screen for the neutrophil dysplasia cases, as compared with the microscopic examination of peripheral blood and/or bone marrow smears.
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How this classification was reachedexpand
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".