Utility of a Cancer Predisposition Screening Tool for Predicting Subsequent Malignant Neoplasms in Childhood Cancer Survivors
Bibliographic record
Abstract
PURPOSE: Childhood cancer survivors (CCS) are at risk of developing subsequent malignant neoplasms (SMNs) resulting from exposure to prior therapies. CCS with underlying cancer predisposition syndromes are at additional genetic risk of SMN development. The McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) tool identifies children with cancer at increased likelihood of having a cancer predisposition syndrome, guiding clinicians through a series of Yes or No questions that generate a recommendation for or against genetic evaluation. We evaluated MIPOGG's ability to predict SMN development in CCS. METHODS: Using the provincial cancer registry (Ontario, Canada), and adopting a nested case-control approach, we identified CCS diagnosed and/or treated for a primary malignancy before age 18 years (1986-2015). CCS who developed an SMN (cases) were matched, by primary cancer and year of diagnosis, with CCS who did not develop an SMN (controls) over the same period (1:5 ratio). Potential predictors for SMN development (chemotherapy, radiation, and MIPOGG output) were applied retrospectively using clinical data pertaining to the first malignancy. Conditional logistic regression models estimated hazard ratios and 95% CIs associated with each covariate, alone and in combination, for SMN development. RESULTS: Of 13,367 children with a primary cancer, 317 (2.4%) developed an SMN and were matched to 1,569 controls. A MIPOGG output recommending evaluation was significantly associated with SMN development (hazard ratio 1.53; 95% CI, 1.06 to 2.19) in a multivariable model that included primary cancer therapy exposures. MIPOGG was predictive of SMN development, showing value in nonhematologic malignancies and in CCS not exposed to radiation. CONCLUSION: MIPOGG has additional value for SMN prediction beyond treatment exposures and may be beneficial in decision making for enhanced individualized SMN surveillance strategies for CCS.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".