Bibliographic record
Abstract
Abstract The 5-year survival for children with cancer has increased enormously. However, years after treatment, childhood cancer survivors are at high risk for developing health and psychosocial long-term side effects due to their previous cancer treatment. The impacts on morbidity, mortality, and society are significant. These impacts can be reduced by optimal long-term follow-up of survivors. Survivorship care that is focused on the detection of treatable disease at an early phase will improve quality of life of survivors by timely interventions. The International Guideline Harmonisation Group (ighg.org) developed evidence-based guidelines for many late effects. However, implementing these guidelines has proven challenging. The availability of follow-up care for adult survivors varies considerably, and many clinics do not offer survivorship care for adult survivors of childhood cancer. High-quality survivorship care requires a multidisciplinary care infrastructure, implementation of guidelines, and development of a survivorship care plan with a detailed summary of treatment and specific knowledge on late effects. This presentation will focus on the translation of knowledge to guidelines and the need for survivorship care. Citation Format: Leontien Kremer. The need for evidence-based survivorship care [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA26.
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 | 0.069 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".