Clinical, sociodemographic, and behavioral factors associated with cumulative burden of morbidity (CBM) among testicular cancer survivors (TCS) in the Platinum study.
Bibliographic record
Abstract
10075 Background: TCS are an important group in which to characterize late effects of cancer and its therapy given their young age at diagnosis and high cure rate. We comprehensively evaluated CBM and identified associated clinical, sociodemographic, and behavioral risk factors among TCS given cisplatin based chemotherapy in a multicenter study. Methods: TCS completed a comprehensive health questionnaire. Responses were grouped into 22 adverse health outcomes (AHO) and graded by severity. A CBM score was calculated based on AHO number and severity, following Geenen et al (JAMA 2007). Multivariable ordinal logistic regression examined the association of clinical, sociodemographic, and behavioral factors with CBM. Variable-based hierarchical clustering identified individual AHOs that co-occurred. Results: Among 1,215 TCS (median age at evaluation 38 y, range 19-68 y; time since chemotherapy 4.6 y), over 20% had a CBM score of high (17%), very high (4%) or severe (0.4%). Most TCS, however, had CBM scores of low (37%), medium (28%), very low (9%) or none (5%). In a multivariable model controlling for time since chemotherapy, older attained age (OR 1.2; 95% CI 1.1 - 1.3), being widowed/divorced/separated (OR 1.8; 95% CI 1.1 - 3.1), having less than college-level education (OR 1.7; 95% CI 1.3 - 2.2), being retired/on disability (OR 2.5; 95% CI 1.2 - 5.3), and receipt of 4 cycles of BEP vs. 4 cycles of EP or 3 cycles of BEP (OR 1.3; 95% CI 1.01 - 1.8) were associated with increased odds of a worse CBM score; vigorous exercise (OR 0.7; 95% CI 0.5 - 0.9) and non-white race (OR 0.6; 95% CI 0.4 - 0.9) were associated with decreased odds. A separate cluster analysis revealed five groups of AHOs: those known to be cisplatin-related (e.g. neuropathy, ototoxicity); metabolic abnormalities (e.g. hypercholesterolemia, diabetes); vascular damage (e.g. stroke); testicular cancer-related (e.g. hypogonadism); and other (e.g. thyroid disease). Conclusions: TCS with factors associated with worse CBM may be candidates for closer monitoring. If confirmed, our cluster analysis showing that groups of conditions tend to co-occur in TCS could provide guidance for survivorship care plans.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".