217 Secondary Malignancy After Radiotherapy for Testicular Cancer: Does the Risk Persist in the Modern Era?
Bibliographic record
Abstract
CARO-ASM 2019all patients aged 18 or older who were diagnosed with cancer between January 2007 and December 2015.Patients were considered exposed if they were screened with ESAS at least once during the study period and their first ESAS screening date was defined as the index date.Each exposed patient was matched randomly to a cancer patient without ESAS using a combination of hard matching (birth year ± 2 years, cancer diagnosis date ± 1 year, cancer type and sex) and propensity score matching (14 variables including cancer stage, treatments received, and comorbidities).Each patient's follow-up time was divided into three phases of care: initial, continuing, or palliative care.A multivariable Andersen-Gill recurrent event model was used to evaluate the effect of ESAS on the rate of healthcare use.Results: The analysis included 128,893 matched pairs that were well balanced on baseline measures.After adjusting for other variables, patients with ESAS had lower rates of both ED visits (HR: 0.92, 95% CI: 0.91-0.93)and hospitalizations (HR: 0.86, 95% CI: 0.85-0.87)compared to patients without ESAS.ESAS screening was associated with lower rates of ED visits and hospitalizations in the initial and palliative phases of care but slightly higher rates in the continuing phase of care.Conclusions: ESAS use is independently associated with decreased rates of both ED visits and hospitalizations in the initial and palliative phases of care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".