14,441 Genomics-Based Validated Automated Comprehensive Clinicopathologic Correlations for Myeloid Neoplasms
Bibliographic record
Abstract
The dataset (Elsafty_Reports_of_Myeloid_Neoplasms_2024) consists of 14,441 comprehensive clinicopathologic correlations (CPCs) for myeloid neoplasms (MN) with corresponding laboratory results. Specialized online platform utilizing these laboratory results automatically generated the diagnostic and prognostic CPCs. These results include complete blood counts (CBC), peripheral blood smear (PBS) findings, blast/promyelocyte count with dysplasia screening by flow cytometry, and molecular results by NGS/PCR. Two Hematopathologists, who performed the CBC and PBS review, collected their 10,794 real-world reports of cases served in Egypt with molecular studies performed in the USA and Europe. These newly-diagnosed cases included 243 chronic myeloid leukemia (CML) cases with 257 mutations/aberrations; 4,567 non-CML MN cases with 7,883 Tiers I/II mutations/aberrations, amino acid changes, and allele frequencies; and 5,984 benign/inconclusive cases with negative NGS for myeloid mutations/aberrations in 66 DNA/RNA genes. Additionally, there are 3,647 CPCs with synthetic genomics simulating all new/follow-up CML cases and complex/rare non-CML MN cases. Stringent validation by 51 international professors/consultants/specialists from multiple medical centres confirmed 100% medical and clerical accuracy, referencing the WHO 5th edition and relevant esteemed publications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".