Synchronous dual hematological malignancies: new or underreported entity?
Bibliographic record
Abstract
BACKGROUND: Patients with a single hematological malignancy may be unexpectedly diagnosed with a clonally unrelated synchronous dual hematological malignancy (SDHM). The presence of a secondary hematological malignancy may be overlooked and only identified in situations presenting with discordant clinical or laboratory findings. Clinical management of these patients can be challenging, in part due to the relatively unknown etiopathology of SDHM and the impact of therapy on the secondary malignancy. OBJECTIVES: To assess, characterize patients with synchronous double hematological malignancies and share our experience with this challenging group of patients. METHODS: We performed a retrospective chart review of 3036 patients with hematological malignancy at our cancer center between February 2013 and July 2017. RESULTS AND DISCUSSION: We identified 46 patients with SDHM, a prevalence of 1.51% among patients diagnosed with any hematological malignancy. We identify several heterogeneous combinations of SDHM comprised of myeloid and/or lymphoid lineages and provide our experience with managing patients with these underreported conditions. CONCLUSION: SDHMs are not uncommon and should be suspected in situations presenting with unusual or unexpected findings.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".