A framework for the clinical implementation of optical genome mapping in hematologic malignancies
Bibliographic record
Abstract
Optical Genome Mapping (OGM) is rapidly emerging as an exciting cytogenomic technology both for research and clinical purposes. In the last 2 years alone, multiple studies have demonstrated that OGM not only matches the diagnostic scope of conventional standard of care cytogenomic clinical testing but it also adds significant new information in certain cases. Since OGM consolidates the diagnostic benefits of multiple costly and laborious tests (e.g., karyotyping, fluorescence in situ hybridization, and chromosomal microarrays) in a single cost-effective assay, many clinical laboratories have started to consider utilizing OGM. In 2021, an international working group of early adopters of OGM who are experienced with routine clinical cytogenomic testing in patients with hematological neoplasms formed a consortium (International Consortium for OGM in Hematologic Malignancies, henceforth "the Consortium") to create a consensus framework for implementation of OGM in a clinical setting. The focus of the Consortium is to provide guidance for laboratories implementing OGM in three specific areas: validation, quality control and analysis and interpretation of variants. Since OGM is a complex technology with many variables, we felt that by consolidating our collective experience, we could provide a practical and useful tool for uniform implementation of OGM in hematologic malignancies with the ultimate goal of achieving globally accepted standards.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 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".