Randomized double blind trial of ciprofloxacin prophylaxis during induction treatment in childhood acute lymphoblastic leukemia in the WK-ALL protocol in Indonesia
Bibliographic record
Abstract
OBJECTIVES: Toxic death is a big problem in the treatment of childhood acute lymphoblastic leukemia (ALL), especially in low-income countries. Studies of ciprofloxacin as single agent prophylaxis vary widely in success rate. We conducted a double-blind, randomized study to test the effects of ciprofloxacin monotherapy as prophylaxis for sepsis and death in induction treatment of the Indonesian childhood ALL protocol. METHODS: Patients were randomized to the ciprofloxacin arm (n = 58) and to the placebo arm (n = 52). Oral ciprofloxacin monotherapy or oral placebo was administered twice a day. All events during induction were recorded: toxic death, abandonment, resistant disease, and complete remission rate. RESULTS: Of 110 patients enrolled in this study, 79 (71.8%) achieved CR. In comparison to the placebo arm, the ciprofloxacin arm had lower nadir of absolute neutrophil count during induction with median of 62 (range: 5-884) versus 270 (range: 14-25,480) × 10(9) cells/L (P < 0.01), greater risks for experiencing fever (50.0% versus 32.7%, P = 0.07), clinical sepsis (50.0% versus 38.5%, P = 0.22), and death (18.9% versus 5.8%, P = 0.05). CONCLUSION: In our setting, a reduced intensity protocol in a low-income situation, the data warn against using ciprofloxacin prophylaxis during induction treatment. A lower nadir of neutrophil count and higher mortality were found in the ciprofloxacin group.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".