National guidelines for the follow-up of childhood cancer survivors
Bibliographic record
Abstract
Members of the Late Effects Taskforce of the Dutch Childhood Oncology Group (DCOG) and of the Haematology-Oncology Section of the Dutch Paediatric Association are involved in the development of guidelines for the follow-up of childhood cancer survivors. The recommendations of these guidelines are based on the best available clinical evidence, current guidelines and clinical experience of late effects specialists. The guidelines will lead to a uniform and standardised post-treatment care and long-term follow-up of childhood cancer survivors in the Netherlands. The information in the guidelines will be of importance for care providers in paediatrics, general medicine, internal medicine, gynaecology/obstetrics as well as for other specialists and particularly for childhood cancer survivors themselves. The information will lead to an increased awareness for all Dutch care providers who are responsible for the health problems of childhood cancer survivors. The development of guidelines for childhood cancer survivors is an important part of a new Dutch project: Late Effects Registry (LATER). Within this new national project patient and treatment data as well as follow-up data on childhood cancer survivors in the Netherlands will be registered. The project LATER aims at: to coordinate and to evaluate care of the survivors, and to stimulate new research in the field of late effects of childhood cancer.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".