Estimating survival time in older adults receiving chemotherapy for advanced cancer.
Bibliographic record
Abstract
e23017 Background: Best-case, worst-case, and typical scenarios for survival time, based on an oncologist’s estimate of expected survival time (EST), have proven accurate in a range of advanced cancers. We sought the accuracy and prognostic significance of such estimates, and of a simple, pragmatic rating of frailty, in older adults starting chemotherapy. Methods: Participants (pts) were aged 65 or older and starting a new line of chemotherapy for advanced cancer. For each pt at baseline, their treating oncologist recorded an individualised estimate of EST (median survival in a group of similar patients), ECOG performance status (PS), and rating of frailty with the single-item, Clinical Frailty Scale from the Canadian Study of Health and Aging. We hypothesised that estimates of EST would be unbiased (approximately 50% of pts would live longer than their EST); imprecise ( < 33% would live for 0.67 to 1.33 times their EST); and, that simple multiples of the EST would provide accurate individualised scenarios for survival time, i.e. approximately 10% of pts would die within ¼ of their EST, 10% would live longer than 3 times their EST, and 50% would live from half to double their EST. We identified independent predictors of observed survival time (OST) with multivariable Cox regression. Results: Baseline characteristics of the 102 pts were: median age 74 (range 65-86), 1st line chemotherapy in 67%, colorectal cancer in 33%, PS 0 or 1 in 80%, and frailty rating of vulnerable to frail in 35%. The median EST was 15 months (range 4-60), median follow-up time was 19 months (range 0-27), and median OST was 15 months (range 0.5-27+). As hypothesized, 54% of pts lived longer than their EST, 30% lived within 0.67 to 1.33 times their EST, 56% lived half to double their EST, and 9% lived ≤1/4 of their EST. Follow-up was too short to observe those who will live ≥3 times their EST. Independent predictors of OST were frailty (HR 2.8, 95%CI 1.6-4.9, p = 0.0004) and EST (HR 0.96, 95%CI 0.93-0.99, p = 0.03). Conclusions: Oncologists’ estimates of EST were unbiased, imprecise, and accurate for formulating scenarios for survival time. A simple, pragmatic rating of frailty by the treating oncologist was a strong predictor of OST even after accounting for their estimate of EST.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".