Risk and Benefit for Targeted Therapy Agents in Pediatric Phase II Trials in Oncology: A Systematic Review with a Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: For research with human participants to be ethical, risk must be in a favorable balance with potential benefits. Little is known about the risk/benefit ratio for pediatric cancer phase II trials testing targeted therapies. OBJECTIVE: Our aim was to conduct a systematic review of preliminary efficacy and safety profiles of phase II targeted therapy clinical trials in pediatric oncology. METHODS: Our protocol was prospectively registered in PROSPERO (CRD42020146491). We searched EMBASE and PubMed for phase II pediatric cancer trials testing targeted agents. We included solid and hematological malignancy studies published between 1 January, 2015 and 27 February, 2020. We measured risk using drug-related grade 3 or higher adverse events, and benefit by response rates. When possible, data were meta-analyzed. All statistical tests were two-sided. RESULTS: We identified 34 clinical trials (1202 patients) that met our eligibility criteria. The pooled overall response rate was 24.4% (95% confidence interval [CI] 14.5-34.2) and was lower in solid tumors, 6.4% (95% CI 3.2-9.6), compared with hematological malignancies, 55.1% (95% CI 35.9-74.3); p < 0.001. The overall fatal drug-related (grade 5) adverse event rate was 1.6% (95% CI 0.6-2.5), and the average drug-related grade 3/4 adverse event rate per person was 0.66 (95% CI 0.55-0.78). CONCLUSIONS: We provide an estimate for the risks and benefits of participation in pediatric phase II cancer trials. These data may be used as an empirical basis for informed communication about benefits and burdens in pediatric oncology research.
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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 | 0.034 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.054 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".