Bibliographic record
Abstract
PURPOSE OF REVIEW: Outcomes for children with cancer have improved dramatically. Although the contribution of disease biology and therapy resistance to treatment failure continues to be a focus of intense research efforts, the role of medication nonadherence on the part of caregivers or patients has been relatively neglected. Efforts to further improve childhood cancer cure rates must include a focus on improving medication adherence. RECENT FINDINGS: Recent studies in children with acute lymphoblastic leukemia have conclusively demonstrated that nonadherence to oral antimetabolite therapy is associated with a significant increase in relapse risk. The impact of nonadherence to other oral medications in acute lymphoblastic leukemia and in other childhood cancers remains unknown. Tools by which clinicians can accurately identify nonadherent families are currently being developed but remain suboptimal. Similarly, while current efforts to develop interventions aimed at increasing adherence rates are underway, their feasibility and effectiveness is still unknown. SUMMARY: Future studies must focus on the development and widespread implementation of methods by which to identify and minimize nonadherence. Doing so will allow for further improve childhood cancer cure outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".