Predictive modeling of therapy induced secondary thyroid malignancies in childhood cancer survivors
Bibliographic record
Abstract
Surgery, radiation therapy and chemotherapy are the primary modes of therapeutic intervention in current clinical practice. The price often paid for effective treatment is the development of a secondary malignancy several decades after successful treatment of the primary tumor, as a result of the mutagenic effects of the initial cancer treatments on normal cells. In this work, we employ a biologically motivated mathematical model to estimate the radiation and chemotherapy-induced relative risks of thyroid malignancies in four childhood cancer study survivors (CCSS) data sets. A sensitivity analysis is performed on various chemotherapy treatment variables to evaluate their impact on second cancer risks. Furthermore, the predictions of radiation and chemotherapy-induced relative risks of secondary thyroid malignancies using the mathematical model are compared against four clinical datasets from the CCSS cohort. Moreover, the extracted average value of growth rate of premalignant cells is 0.8175 (d−1) and the extracted chemotherapy-induced mutation rate is of the order of 10−10 (per unit of chemotherapeutic dose). In addition, our model predictions of sequential therapy induced carcinogenic risks are in line with the clinical data in secondary thyroid cancers. Our in silico risk predictions can provide insight into the impact of therapy sequencing on secondary cancer risks, while at the same time eliminating the primary tumor. These findings might potentially guide clinicians in developing optimal treatment regimens that minimize secondary cancer risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".