Bibliographic record
Abstract
BACKGROUND: Proper diagnosis of myeloid leukemia cutis (LC) is of great clinical importance but can be difficult because no single immunohistochemical marker is adequately sensitive or specific for definitive diagnosis. Thus, a broader panel of markers is often desirable. CD163 is highly specific for normal and neoplastic cells of the monocyte/histiocyte lineage. In this study, we examined the value of CD163 in the diagnosis of acute myeloid LC. METHODS: A total of 34 cases, including 18 cases of myelomonocytic or monocytic LC, 10 cases of myeloid LC without monocytic component and 6 cases of acute lymphoblastic leukemia/lymphoma (ALL), were stained with CD163. RESULTS: CD163 was expressed in 8 of 18 (44%) of myelomonocytic or monocytic LC and 1 of 10 (10%) of other myeloid LC, but in none of the ALL cases (0/6). CD163 was highly specific (90%) for myeloid LC with a monocytic component, but showed low sensitivity in the diagnosis of both myeloid LC in general (24%) and myeloid LC with a monocytic component (44%). CONCLUSIONS: Our results suggest that CD163 has utility as a specific marker for myeloid LC in conjunction with currently used immunohistochemical stains, but should not be used alone for diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 |
| 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".