Clinical value of next‐generation sequencing compared to cytogenetics in patients with suspected myelodysplastic syndrome
Bibliographic record
Abstract
Next-generation sequencing (NGS) increasingly influences diagnosis, prognosis and management of myelodysplastic syndrome (MDS). In addition to marrow morphology and flow cytometry, our institution performs cytogenetics (CG) and NGS-based testing routinely in patients with suspected MDS. We evaluated the relative value of NGS in the assessment of patients with suspected MDS. We initially compared the diagnostic and prognostic information derived from CG and NGS in 134 patients. NGS enhanced the diagnostic yield compared to CG for clonal myeloid disorders (sensitivity 77% vs. 42·2%; specificity 90·2% vs. 78%; positive predictive value 92·8% vs. 76%; and negative predictive value 70·8% vs. 45·5%). The identification of poor prognosis mutations by NGS altered risk category in 27/39 (69·2%) patients with MDS with good/intermediate risk CG. Subsequently, we prospectively evaluated 70 patients with suspected MDS using an 'NGS-first approach' with CG restricted to samples with morphological abnormalities. We rarely identified mutations or CG abnormalities in patients without dysplastic features. NGS has a superior diagnostic performance compared to CG in patients with suspected MDS. We estimate that by using an 'NGS-first approach' we could reduce karyotyping by approximately 30%.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".