Optical genome mapping for structural variation analysis in hematologic malignancies
Bibliographic record
Abstract
Optical genome mapping (OGM) is a technology that is rapidly being adopted in clinical genetics laboratories for its ability to detect structural variation (e.g., translocations, inversions, deletions, duplications, etc.) and replace several concurrent standard of care techniques (karyotype, fluorescence in situ hybridization, and chromosomal microarray). OGM can dramatically simplify lab workflow by reducing multiple tests (conventional karyotype, fluorescence in situ hybridization [FISH], and chromosomal microarray) into one test. The superior ability to detect structural variation across the genome removes the need for reflex FISH studies, which can dramatically reduce cost and turnaround time per sample. Parallel studies of OGM versus standard of care testing have demonstrated it can detect and resolve more abnormalities than karyotyping or FISH. However, like many molecular tests that normalize copy number it can have difficulty with non-diploid karyotypes. This Test of the Month review will summarize how the technique works, review the strengths and weaknesses of OGM compared to standard of care techniques and illustrate how the technique is likely to change front line testing in many hematologic malignancies-including summarizing the clinical utility in acute myeloid leukemia, myelodysplastic syndromes, and B cell acute lymphoblastic leukemia.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".