Case Report: Effects of multiple myeloma therapy on essential thrombocythemia and vice versa: a case of synchronous dual hematological malignancy
Bibliographic record
Abstract
Background Dual hematological malignancies, asynchronous or synchronous, are underrecognized entities and are usually suspected when clinical, hematological, or biochemical features cannot be explained by the primary malignancy alone. We present a case of synchronous dual hematological malignancies (SDHMs), where the patient was diagnosed with symptomatic multiple myeloma (MM) and essential thrombocythemia (ET), when excessive thrombocytosis occurred following initiation of MPV (melphalan–prednisone–bortezomib) antimyeloma therapy. Case description An 86-year-old woman presented to the emergency in May 2016 with confusion, hypercalcemia, and acute kidney injury. She was diagnosed with free light chain (FLC) lambda and Immunoglobulin G (IgG) lambda MM and started MPV (standard of care at that time) treatment with darbopoietin support. At diagnosis, she had normal platelet count since the ET was likely masked by bone marrow suppression due to active MM. After she reached stringent complete remission with no MP detected on serum protein electrophoresis or immunofixation, we noticed that her platelet counts increased to 1,518,000 × 109/L. She was tested positive for mutation in exon 9 of calreticulin (CALR). We concluded that she had concomitant CALR-positive ET. After bone marrow recovery from MM, the ET became clinically apparent. We started hydroxyurea for ET. Treatment for MM with MPV did not affect the course of ET. Presence of concomitant ET did not decrease the efficacy of sequential antimyeloma therapies in our elderly and frail patient. Conclusion The possible mechanism underlying the occurrence of SDHMs is unclear but is likely due to stem cell differentiation defects. SDHMs can be challenging to treat and warrant several considerations. In the absence of clear guidelines on how to manage SDHMs, management decisions depend on several factors including disease aggressiveness, age, frailty, and comorbidities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".