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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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 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".