Characteristics of clinical trials for adolescent and young adult (AYA) cancer patients (pts) in Ontario.
Bibliographic record
Abstract
e17525 Background: Outcomes of pediatric and adult cancer pts have improved more than outcomes of AYA pts. Reduced access of AYA pts to clinical trials may partially account for this. Methods: The 10 cancers of highest annual incidence for pts ages 15-29 were identified using the 2005-09 SEER data base and searched on ClinicalTrials.gov (2012/OCT/27) using restriction terms “recruiting”, “intervention studies”, “Canada-Ontario”, and “Adult (18-65)”. Among trials identified, variables compared were sponsor (industry; not industry), intent (curative; palliative; other), endpoints (survival; disease control-adverse event; other), previous therapy (none; other), age (max < 29; max>29 but <50; max>50), intervention (drug; RT+/-drug; other) and randomized design (yes; no). Comparisons used a 2-tailed Fisher’s exact test. Results: We identified 139 trials, including (incidence rank order in bracket): breast-41 (8), non Hodgkin’s lymphoma-36 (4), CNS-30 (6) and acute lymphoblastic leukemia (ALL)-21[10]. There were 45 trials available for the other 6 indications; some trials permitted multiple indications. Most common features were non-industry sponsorship (58%), palliative intent (65%), disease control-adverse event primary endpoint (82%), not restricted to 1st line therapy (76%), max age>50 (69%), drug-only intervention (78%) and non-randomized design (56%). Among all trials, those with an age restriction did not significantly differ from trials with an age maximum of >50. Industry sponsorship was associated with fewer non-drug-only trials (2% vs. 36%; p<0.0001), curative trials (12% vs. 41%; p=0.0002) and age-restricted trials (10% vs. 48%; p<0.0001). CNS and ALL trials were more likely to restrict age eligibility (60% vs. 13%; p<0.0001 for CNS; 52% vs. 18%; p<0.0001 for ALL) and have non-industry sponsorship (90% vs. 49%; p<0.0001 for CNS; 76% vs. 24%; p=0.09 for ALL). Conclusions: Availability of clinical trials for Ontario AYA pts is very heterogeneous and no correlation is seen between AYA cancer-type incidence and number of trials. While CNS cancers and ALL appear unique, access and characteristics of trials for other AYA pts appear to relate to determinants associated with older patients.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".