Outpatient Supportive Care Following Chemotherapy for Acute Myeloblastic Leukemia
Bibliographic record
Abstract
Treatment of acute myeloid leukemia (AML) involves aggressive myelosuppressive chemotherapy that is generally administered on an inpatient basis. In our centre, AML therapy has been initiated in hospital and followed by early outpatient supportive care according to guidelines established in 1996. We conducted a review of all patients presenting with AML in our centre between January 1996 and July 1998 to evaluate the safety and feasibility of early outpatient supportive care. Nineteen consecutive patients treated with induction chemotherapy were analyzed. Patients were treated with cytosine arabinoside and an anthracycline as aggressive AML induction therapy with the intent for early discharge. Ten patients (53%) were discharged within 10 days of starting induction chemotherapy (median 4.5 days). Reasons for remaining in hospital included sepsis, serious medical complications, and social and geographic factors. Patients discharged early had a median of 1.5 readmissions (range 0-3), but had 30% fewer in-hospital days than inpatients (p = 0.03), and 57% fewer days of in-hospital antibiotic therapy (p = 0.01). There were no significant differences in transfusion requirements or episodes of febrile neutropenia between the two groups. Thirty-one cycles of consolidation therapy were administered to the 18 patients who survived induction. Early discharge from hospital was achieved for 30 cycles (97%). Nine cycles of consolidation chemotherapy were delivered using outpatient intravenous infusion pumps (29%). This study supports the feasibility and safety of early discharge and outpatient supportive care following chemotherapy for AML.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".