L’arsenic, un agent toxique? Utilisation de l’arsenic dans le traitement des leucémies myéloïdes aiguës de type M3
Bibliographic record
Abstract
Resume Cet article discute du cas d’un patient, M. D., âge de 60 ans qui est atteint de leucemie myeloide aigue (type M3) depuis 1995 et pour laquelle il a deja recu plusieurs traitements de chimiotherapie incluant la daunorubicine, la cytarabine et l’acide tout-trans retinoique (ATRA). Le patient a fait une premiere rechute en 1998, puis une seconde en mars 2001 alors qu’il recevait l’ATRA a une dose de 40 mg deux fois par jour de facon cyclique (2 semaines sur 6) et 15 mg per os de methotrexate 1 fois par semaine. A la suite de cette seconde rechute sous l’ATRA, le patient a ete admis a l’unite d’hematologie pour une neutropenie. L’equipe traitante a decide de debuter un traitement avec l’arsenic. Ce dernier medicament a ete debute en mai 2001 a une dose de 10 mg une fois par jour par voie intraveineuse. Abstract Prevention and management of osteoporosis This article discusses the case of a 60-year-old patient suffering from acute promyelocytic leukemia since 1995 for which he received multiple chemotherapy treatments including daunorubicine, cytarabine and all-trans retinoic acid (ATRA). The patient had his first relapse in 1998 and a second in March 2001 while he was receiving ATRA at 40 mg twice a day (2 weeks of 6) and 15 mg of methotrexate once a week. Following the second relapse, the patient was admitted to the hematology unit for neutropenia. The medical team decided to start at that point an arsenic trioxide treatment. The medication was started in May 2001 with an intravenous dose of 10 mg once daily. Furthermore, the article reviews the place of arsenic trioxide in the current management of the acute promyelocytic leukemia patients.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".