Abstract B60: Exposure assessment among an adult population on radiation therapy, chemotherapy, and other cancer therapies in childhood
Bibliographic record
Abstract
Abstract Childhood cancer therapies are known risk factors for the development of second primary cancers. They are also suggested risk factors for late adverse health effects. Until now, there is no established questionnaire to retrospectively assess exposure to cancer therapies in childhood among adults. Therefore, we aim to validate a new self-administered questionnaire. The study population consists of 438 former childhood cancer patients of the KiKme study. Participants are asked whether they had received cancer therapies and how often and with which dose they were treated. Used medications and affected body regions are inquired. Questionnaire data are used to compute cytotoxic drugs dose of chemotherapy, taking patients’ weight and height into account, and to reconstruct individual organ doses. For validation, self-reports are compared to data of cancer therapies of 178 patients from hospitals and clinical studies. Quality assessment for binary variables are performed by measuring sensitivity and specificity. AUC and ROC curve are used for graphical comparison. The validity is analyzed by the positive (PPV) and negative predictive value (NPV). Cohen’s Kappa (κ) is used to measure the concordance between the two assessments. Continuous variables are tested for validity by the intraclass correlation coefficient. A Bland-Altman plot is used to consider the patterns of disagreement between the measurements. Influencing factors (e.g., number of neoplasms, sex, sociodemographic factors, comorbidities, time since cancer treatment) on the dichotomous outcome variable “degree of agreement” are analyzed using logistic regression. If the questionnaire is reliable, logistic regression and mixed models will be used to estimate possible risk associations with cancer therapies. A perfect agreement between questionnaire and therapy data was found on whether a chemotherapy was received (κ = 1.00). The agreement for exposure to radiotherapy was lower, but in the upper substantial area (κ = 0.77). For radiotherapy, sensitivity (94%) and PPV (96%) of the questionnaire were at a very high level. Specificity (85%) and NPV (80%) were less precise. The agreement for exposure to radiotherapy was higher in participants with one cancer (κ = 0.82) compared to participants with more than one diagnosis (κ = 0.62). The odds ratios for agreement were 0.5 (0.1; 1.8) for participants with two vs. one diagnosis, 1.3 (0.3; 4.9) for men vs. women, 10.9 (1.7; 71.9) for age over 36.4 years (median) vs. younger participants, 2.2 (0.6; 9.0) for high vs. low education, 0.3 (0.1; 1.4) for over 26.5 years of follow-up (median) vs. less, 2.4 (0.6; 9.5) for existing vs. nonexisting comorbidities. In conclusion, the new developed questionnaire seems to be reliable for the retrospective assessment of binary exposure to cancer therapies in childhood, especially for chemotherapy. However, for radiotherapy older participants showed a significant higher agreement. All other tested variables showed no significant influence. Citation Format: Lara Kim Brackmann, Caine Lucas Grandt, Heike Schwarz, Irene Schmidtmann, Thomas Hankeln, Danuta Galetzka, Sebastian Zahnreich, Peter Scholz-Kreisel, Maria Blettner, Heinz Schmidberger, Manuela Marron. Exposure assessment among an adult population on radiation therapy, chemotherapy, and other cancer therapies in childhood [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B60.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".