Feasibility of a registry for standardized assessment of long-term and late-onset health events in survivors of childhood and adolescent cancer
Bibliographic record
Abstract
Childhood and adolescent cancer survivors are at risk for chronic medical conditions. Longitudinal studies help to understand their development and course. We hypothesize that collecting follow-up data according to the modified CTCAE criteria and embedded in regular care, is feasible and results in a rich database. We recruited 50 Swiss survivors treated at our institution between 1992 and 2015, who completed their treatment and are still alive. Information on cancer diagnosis, treatment, and medical conditions from follow-up visits, graded according to the modified CTCAE criteria, were added in the database. We described the cohort, assessed the prevalence of medical conditions at the most recent visits and the time needed for data entry. Survivors had a median age of 10 years at diagnosis with 16 years of follow-up. 94% of survivors suffered from at least one medical condition. We registered 25 grade 3 or 4 conditions in 18 survivors. The time needed for data entry at enrollment was < 60 min in most survivors and much less for follow-up visits. Standardized assessment of medical conditions is feasible during regular clinical care. The database provides longitudinal real-time data to be used for clinical care, survivor education and research.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".