Telomere Content and Risk of Second Malignant Neoplasm in Survivors of Childhood Cancer: A Report from the Childhood Cancer Survivor Study
Bibliographic record
Abstract
PURPOSE: Shorter constitutional telomere length has been associated with increased cancer incidence. Furthermore, telomere shortening is observed in response to intensive chemotherapy and/or ionizing radiation exposure. We aimed to determine whether less telomere content was associated with treatment-related second malignant neoplasms (SMN) in childhood cancer survivors. EXPERIMENTAL DESIGN: Using a nested case-control design, 147 cancer survivors with breast cancer, thyroid cancer, or sarcoma developing after treatment for childhood cancer (cases) were matched (1:1) with childhood cancer survivors without a SMN (controls). Cases and controls were matched by primary cancer diagnosis, years since diagnosis, age at the time of sample collection, years of follow-up from childhood cancer diagnosis, exposure to specific chemotherapy agents, and to specific radiation fields. We performed conditional logistic regression using telomere content as a continuous variable to estimate ORs with corresponding 95% confidence intervals (CI) for development of SMN. ORs were also estimated for specific SMN types, i.e., breast cancer, thyroid cancer, and sarcoma. RESULTS: There was an inverse relationship between telomere content and SMN, with an adjusted OR of 0.3 per unit change in telomere length to single-copy gene ratio (95% CI, 0.09-1.02; P = 0.05). Patients with thyroid cancer SMN were less likely to have more telomere content (OR, 0.04; 95% CI, 0.00-0.55; P = 0.01), but statistically significant associations could not be demonstrated for breast cancer or sarcoma. CONCLUSIONS: A relation between less telomere content and treatment-related thyroid cancer was observed, suggesting that shorter telomeres may contribute to certain SMNs in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".