SP-0243: Cost-effectiveness data to guide treatment decisions for elderly patients: focus on radiotherapy
Bibliographic record
Abstract
As a disease of the elderly, cancer poses a unique public health problem worldwide.Elderly patients with cancer are less likely to receive guideline-based treatment and/or participate in clinical trials.At the individual patient level, competing risk, perceived efficacy of treatment, and various levels of patient/physician preferences all contribute to heterogeneity in treatment decision-making.At the population level, the economic impact of this variability is significant.Costs incurred in the prevention, diagnosis, treatment and surveillance of cancer are rising at a rate disproportionate to what healthcare systems are able to afford.Cost-effectiveness research can be employed to determine the suitability of radiotherapy in elderly cancer populations through modeling or in the context of clinical trials.Using stereotactic radiotherapy in early stage lung cancer as an example, the principals of cost-effectiveness research will be explored.Concepts such as cost calculations, quality adjusted life expectancy, utilities, and incremental cost effectiveness ratios will be introduced.
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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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".