Implementation of an NGS‐based sequencing and gene fusion panel for clinical screening of patients with suspected hematologic malignancies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: The diagnosis of hematologic malignancies integrates multiple diagnostic and clinical disciplines. Historically, targeted (single-analyte) genetic testing has been used as reflex to initial prescreening by other diagnostic modalities including flow cytometry, anatomic pathology, and clinical cytogenetics. Given the wide range of mutations associated with hematologic malignancies a DNA/RNA-based NGS panel can provide a more effective and economical approach to comprehensive testing of patients as an initial, tier-1 screen. METHODS: Using a cohort of 380 patients, we performed clinical validation of a gene panel designed to assess 40 genes (DNA), and 29 fusion driver genes with over 600 gene fusion partners (RNA), including sample exchange data across three clinical laboratories, and correlation with cytogenetic testing results. RESULTS: The clinical validation of this technology demonstrated that its accuracy, sensitivity, and specificity are comparable to the majority of targeted single-gene approaches, while assessment of the initial patient cohort data demonstrated a high diagnostic yield of 50.5%. CONCLUSIONS: Implementation of a tier-1 NGS-based protocol for gene panel screening provides a comprehensive alternative to targeted molecular testing in patients with suspected hematologic malignancies, with increased diagnostic yield, scalability, reproducibility, and cost effectiveness, making it ideally suited for implementation in clinical laboratories.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it