External validation of two predictive scores of chemotherapy toxicities among older patients with solid cancer, from ELCAPA prospective cohort.
Bibliographic record
Abstract
12050 Background: Severe chemotherapy toxicities are frequent among older patients, and may have a major impact on mortality, comorbidities, and quality of life. Two scores were developed to predict severe toxicities: Chemotherapy Risk Assessment Scale for High-age patients (CRASH) score, and Cancer and Aging Research Group Study (CARG) score. The main objective of the present study was to evaluate the predictive value of both scores on an external cohort. Secondary objective was to identify individual predictive factors of severe chemotherapy toxicities. Methods: The Elderly Cancer Patients (ELCAPA) survey consists in a prospective cohort including patients aged 70 years or older referred for a geriatric assessment (GA) before anticancer treatment, such as chemotherapy for solid cancer. CARG and CRASH score were retrospectively collected. Main endpoint was grade 3/4/5 toxicities for CARG-score, hematologic grade 4/5 and non-hematologic grade 3/4/5 toxicities for CRASH-score. Calibration and discrimination (Area Under ROC Curve, AUC) were evaluated. Results: From July 2010 to March 2017, 248 patients were included. Among them, 150 (61%) experienced severe toxicity as defined in CARG study, and 126 (51%) as defined in CRASH study. There was no increased risk of toxicity in intermediate and high risk groups of CARG-score compared to low risk group (OR = 0.3, IC95% [0.1 – 1.4], p= 0.1; and OR = 0.4, IC95%[0.1 – 1.7], p= 0.2 respectively, AUC-ROC = 0.55). Similarly, there was no more risk of severe toxicities in intermediate low, intermediate high, and high risk groups compared to low risk groups of CRASH combined score (respectively OR = 1, IC95% [0.3 – 3.6], p= 0.99; OR = 1, IC95% [0.3 – 3.4], p= 0.9; OR = 1.5, IC95% [0.3 – 8.1], p= 0.67; AUC-ROC = 0.52). A multivariate predictive model including cancer type, performance status (PS 0 vs. PS 1-2), number of severe comorbidities (Cumulative Illness Rating Scale for Geriatrics, CIRS-G, ≥1 grade 3 or 4 comorbidity), body mass index (BMI > 25 kg/m² protective vs. normal BMI), and Chemotox score (1 vs. 0) had an AUC of 0.78. Conclusions: Neither CARG nor CRASH score was predictive of severe chemotherapy toxicities in the ELCAPA cohort. There is a need to identify new predictors of chemotherapy toxicity in older patients with solid cancers.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".