How we approach early phase clinical trial and off-label therapy consults in pediatric oncology: The New Agents and Innovative Therapy (NAIT) team experience
Bibliographic record
Abstract
In the context of hard-to-cure disease, pediatric oncologists may have to explore novel therapy options and explain their rationale, risks and constraints to patients and caregivers. The New Agents and Innovative Therapy (NAIT) program at Hospital for Sick Children in Toronto facilitates patient enrollment in clinical trials as well as access to innovative therapies outside of clinical trials. Here, we summarize our experience with helping patients, caregivers, and their primary oncology team navigate information and access to new therapeutic options through enrollment in clinical trials but also off-label and compassionate use. We expose our approach to exploring clinical trial and other therapy options. We share lessons learned from clinical practice regarding the specific role of NAIT consultant, as opposed to the primary oncologist or the disease expert. We expand on ways to communicate regarding the objectives of early phase clinical trials, their methods and the important commitment asked from participants. We describe our views on equipoise, uncertainty and hope in this very specific practice. We support a model of shared decision making and empowerment of patients and caregivers. We also detail the use, benefits and challenges of virtual care applied to NAIT consults. Overall, we hope to contribute and facilitate the NAIT practice not only for trained trialists but also less-specialized teams. • Innovative therapy options should be explored in the context of hard-to-cure cancer. • Innovative therapies can be accessed through clinical trials and “out-of-trial”. • Communication with patients and caregivers is paramount is this context. • Virtual care applied to these consults has benefits and challenges.
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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 | 0.035 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".