Acute Myelogenous Leukemia with t(8;21)—Identification of a Specific Immunophenotype
Bibliographic record
Abstract
Association between certain surface markers and acute myelogenous leukemia (AML) with t(8;21) has been described. The specificity and the predictive values of these markers have never been assessed. In this study, we aimed, to explore whether a specific pattern could predict for this translocation. Of 405 consecutive AML, 18 (4.4%) had the t(8;21). Patients with this cytogenetic abnormality showed higher frequency of CD34 (P = 0.003), HLA-DR (P = 0.03), Tdt (P = 0.02), CD19 (P < 0.0001), and CD56 (P < 0.0001) and lower CD33 (P = 0.0001). Taken singly, the sensitivity of these markers for AML with t(8;21) ranged between 39 and 100% with CD34+ having the highest and CD33- having the lowest and the positive predictive values (PPV) ranged between 5 and 21% with CD19+ having the highest and HLA-DR+ having the lowest. When combinations of different markers were analyzed by multivariate analysis, the pattern CD34+/HLA-DR+/MPO+ was found to have the highest sensitivity (100%) with a PPV of 14% and the pattern CD34+/CD19+/CD56+ had the highest PPV (100%) with a sensitivity of 67%. We conclude that AML with t(8;21) is better identified by a combination of markers than by a single antigen pattern, the absence of CD34+, HLA-DR+ or MPO+ would preclude and the expression of the pattern CD34+/CD19+/CD56+ is highly predictive and could serve as a screening criteria for the t(8;21).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".