CNS Relapses of Acute Promyelocytic Leukemia after All- <i>Trans</i> Retinoic Acid
Bibliographic record
Abstract
OBJECTIVE: To review the role of all-trans retinoic acid (ATRA) and arsenic trioxide in central nervous system (CNS) relapses of acute promyelocytic leukemia (APL). CASE SUMMARY: A 69-year-old white man diagnosed with APL presented with bleeding diathesis. His molecular and cytogenetic studies were positive for promyelocytic leukemia-retinoic acid receptoralpha (PML-RARalpha) and t(15;17) transformation. Complete molecular and cytogenetic remission was achieved with ATRA, daunorubicin, and cytarabine. Within 6 months, the patient was readmitted for investigation of severe global headaches and an ataxic gait. His peripheral blood and cerebral spinal fluid were positive for PML-RARalpha fusion protein. Intrathecal chemotherapy and radiation, as well as ATRA, were the main treatment modalities provided. Molecular and cytogenetic remission was again obtained. Three months later, a second relapse occurred in the CNS and the peripheral blood. DISCUSSION: APL is typically treated with anthacycline-based chemotherapy and ATRA. Approximately 85-95% of patients achieve complete remission (CR); however, the relapse rate has been reported to be about 30-40%. A thorough literature search (MEDLINE, EMBASE, CANCERLIT, 1966-January 2002) revealed only 54 cases of extramedullary disease, of which 35 involved the CNS. CONCLUSIONS: The introduction of ATRA has improved patient survival dramatically. APL relapse, in general, has been in part attributable to repetitive or prolonged exposure to ATRA and the possibility of additional chromosomal changes, making the disease more refractory to treat. Given the evidence, one could argue that, with repeated ATRA treatment, CR duration may be shortened. However, limited data are available to guide the appropriate management of APL relapsed to the CNS with either ATRA, chemotherapy, or arsenic trioxide. In our opinion, treatment using arsenic trioxide is an unconventional option worthy of exploring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".