Prediction of second malignant neoplasm incidence in a large cohort of long‐term survivors of childhood cancers
Bibliographic record
Abstract
BACKGROUND: The ability to predict adverse-event occurrences accurately in long-term survivors of childhood cancer is of high importance in late effects research, both clinically and methodologically. PROCEDURE: This article considers a statistical prediction of future events in a cohort, taking second malignant neoplasm (SMN) incidence in a large cohort of long-term childhood cancer survivors as an example. The method consists of dividing the follow-up period of the cohort into two non-overlapping periods, using the first period as "training data," with which we model the patterns of SMN occurrences in the cohort, and the subsequent period as "testing data," with which we validate the model based on the training data. Future predictions are also applied beyond the testing-data period to calculate the SMN incidence of the cohort in the next five years for overall and specific types of SMNs. RESULTS: The proposed statistical prediction is shown empirically to perform well with respect to the prediction accuracy. Overall, the models were able to predict the future second cancers rates very well, with exceptions of a few cancer types that had very small observed counts in the testing period. CONCLUSIONS: Our proposed statistical method predicts future events in a cohort of long-term childhood cancer survivors and, as such, is a useful tool for late effects research on childhood cancer survivors.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".