Conventional Karyotyping and Fluorescence <i>In Situ</i> Hybridization for Detection of Chromosomal Abnormalities in Multiple Myeloma
Bibliographic record
Abstract
Background: hybridization (FISH) analysis and/or G-banded karyotyping. The two methods produce unique and overlapping information, and the clinical utility of using both is investigated here. Methods: Seventy patients diagnosed with MM at a hospital in Southern California were retrospectively reviewed for the FISH and G-banded karyotyping results obtained from bone marrow specimens. Results: Karyotype was normal in 71% (50/70), abnormal in 27% (19/70), and inadequate in 1% (1/70). Among patients with abnormal karyotype, FISH provided additional information about genetic aberrations in 95% of cases (18/19). Among cases with abnormal FISH, karyotype provided additional information about genetic aberrations in 27% of cases (18/66). When numerical abnormalities were present (detected by FISH and/or karyotype), FISH detected them in 95% (54/57), of which karyotype missed 70% (38/54) of the time. Karyotyping detected numerical abnormalities in 33% (19/57), which FISH missed 16% (3/19) of the time. Conclusions: Karyotyping and FISH analysis in MM each provide unique information. For most patients, performing both tests together will provide more information than either test alone.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".