Neuropsychological outcomes of a randomized trial of prednisone versus dexamethasone in acute lymphoblastic leukemia: Findings from Dana-Farber Cancer Institute All Consortium Protocol 00-01
Bibliographic record
Abstract
BACKGROUND: Dexamethasone is more efficacious than prednisone in the treatment of acute lymphoblastic leukemia (ALL), but has also been associated with greater toxicity. We compared neuropsychological outcomes for patients treated on DFCI ALL Consortium Protocol 00-01, which included a randomized comparison of the two steroid preparations during post-induction therapy in children and adolescents with ALL. PROCEDURE: Between 2000 and 2005, 408 children with standard-risk or high-risk ALL treated on Dana-Farber Cancer Institute Consortium Protocol 00-01 were randomly assigned to prednisone or dexamethasone administered as 5-day pulses every 3 weeks for 2 years, beginning at week 7 of treatment. Blinded neuropsychological testing was completed for 170 randomized patients (prednisone, N = 76; dexamethasone, N = 94), all of whom were in continuous complete remission after completion of therapy. RESULTS: Outcomes were comparable for most variables, although patients on the dexamethasone arm performed more poorly on a measure of fluid reasoning (P = 0.02). They also tended to be more likely to be enrolled in special education (dexamethasone, 33% vs. prednisone, 20%, P = 0.09). CONCLUSIONS: Dexamethasone has well documented benefit in treatment of ALL. Although formal testing provided little indication of increased risk for neurotoxicity relative to prednisone, the somewhat greater utilization of special education services by patients treated with dexamethasone merits further investigation.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".