Bibliographic record
Abstract
Extramedullary relapse (EMR) of acute promyelocytic leukemia (APL) is a rare entity, with predilection to involve the central nervous system (CNS). Risk factors include leukocytosis of > 10 × 10 9 /L, bcr3 isoform, microgranular variant, age > 45 years and development of subarachnoid hemorrhage (SAH) during induction therapy. We report a case of APL who completed induction and consolidation therapy but subsequently relapsed with leptomeningeal involvement. Retrospectively, we identified several risk factors for EMR in our patient. Interestingly, the use of all-trans retinoic acid has recently been associated with higher risk of EMR possibly due to up-regulation of adhesion molecules on the surface of the leukemic cell, resulting in their passage through the endothelium to extramedullary tissues. However, data remain conflicting in that regard. Although universal CNS prophylaxis has been suggested, the low incidence of EMR among APL patients renders this strategy less attractive. Nonetheless, active surveillance and CNS prophylaxis may be considered in patients at high risk for EMR, particularly in those of SAH during induction therapy. Further research is needed to evaluate the effectiveness and safety of this strategy. World J Oncol. 2014;5(2):77-80 doi: http://dx.doi.o rg/10.14740/wjon761 w
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".