Growth Hormone Treatment and Risk of Second Neoplasms in the Childhood Cancer Survivor
Bibliographic record
Abstract
CONTEXT: GH deficiency is common in childhood cancer survivors. In a previous report, although we did not find an increase in the risk of disease recurrence in survivors treated with GH, GH-treated survivors did have an increased risk of developing a second neoplasm (SN) (rate ratio, 3.21). OBJECTIVE: In this analysis, we have reassessed the risk of GH-treated survivors developing an SN after an additional 32 months of follow-up. DESIGN AND SETTING: We conducted a retrospective cohort multicenter study. PATIENTS: Among a total of 14,108 survivors who were enrolled in the Childhood Cancer Survivor Study, a retrospective cohort of 5-yr survivors of childhood cancer, we identified 361 who were treated with GH. MAIN OUTCOME: We assessed the risk of developing an SN. RESULTS: During the extended follow-up, five new SN developed in survivors treated with GH, for a total of 20 SN, all solid tumors. Using a time-dependent Cox model, the rate ratio of GH-treated survivors developing an SN, compared with non-GH-treated survivors, was 2.15 (95% confidence interval, 1.3-3.5; P < 0.002). Meningiomas were the most common SN (n = 9) among the GH-treated group. CONCLUSION: Although cancer survivors treated with GH appear to have an increased risk of developing SN compared with survivors not so treated, the elevation of risk due to GH use appears to diminish with increasing length of follow-up. Continued surveillance is essential.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".