Clinical development success rates and social value of pediatric Phase 1 trials in oncology
Bibliographic record
Abstract
OBJECTIVES: Drug development trials must fulfill social value requirement but no estimates of value provided by pediatric Phase 1 trials in oncology exist. These trials involve a particularly vulnerable population. Our objective was to assess of surrogates of social value of Phase 1 trials performed in pediatric oncology: rates of approval of tested interventions, transition to further phases of testing and citation in subsequent primary research reports. METHODS: We performed an analysis on a subset of eligible trials included in a previous meta-analysis. That study systematically searched EMBASE and PubMed for small sample size, non-randomized, dose escalation pediatric cancer Phase 1 studies of any malignancy, assessing chemotherapy and/or targeted therapy and looked at risk and benefit. The current analysis assessed all studies in that review published between January 1st 2004 and December 31st 2013 for predictors of social value. This time range allowed for at least five years of subsequent development activity. Sources of data included FDA and EMA medicine databases (for approval), ClinicalTrials.gov and EU Clinical Trials Register (for transition) and Google Scholar (for citation). RESULTS: One hundred thirty-nine trials enrolling 3814 patients met the eligibility criteria. Seven trials (5%) led to drugs being registered for pediatric use in therapy of cancer. Fifty-two (37%) transitioned to later phases of pediatric oncology trials according to ClinicalTrials.gov and/or EU Register. Over 90% of trials were cited by at least one subsequent primary research report or systematic review. Most of the citations were preclinical studies. CONCLUSIONS: Our analysis shows that treatments tested in pediatric Phase 1 trials in oncology have low rates of regulatory approval. However, a large proportion of Phase 1 trials inform further testing and development of tested interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.420 | 0.738 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".