Toxicity assessment of molecularly targeted drugs incorporated into multiagent chemotherapy regimens for pediatric acute lymphocytic leukemia (ALL): Review from an international consensus conference
Bibliographic record
Abstract
One of the challenges of incorporating molecularly targeted drugs into multi-agent chemotherapy (backbone) regimens is defining dose-limiting toxicities (DLTs) of the targeted agent against the background of toxicities of the backbone regimen. An international panel of 22 pediatric acute lymphocytic leukemia (ALL) experts addressed this issue (www.ALLNA.org). Two major questions surrounding DLT assessment were explored: (1) how toxicities can be best defined, assessed, and attributed; and (2) how effective dosing of new agents incorporated into multi-agent ALL clinical trials can be safely established in the face of disease- and therapy-related systemic toxicities. The consensus DLT definition incorporates tolerance of resolving Grade 3 and some resolving Grade 4 toxicities with stringent safety monitoring. This functional DLT definition is being tested in two Children's Oncology Group (COG) ALL clinical trials.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".