Implications of Cancer Staging Uncertainties in Radiation Therapy Decisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Radiation therapy (RT) for cancer is a critical medical procedure that occurs in a complex environment involving numerous health professionals, hardware, software, and equipment. Uncertainties and potential incidents can lead to inappropriate administration of radiation to patients, with sometimes catastrophic consequences such as premature death or appreciably impaired quality of life. The authors evaluate the impact of incorrectly staging (i.e., estimation of extent of cancer) breast cancer patients and resulting inappropriate treatment decisions. METHODS: The authors employ analytic and simulation methods in an influence-diagram framework to estimate the probability of incorrect staging and treatment decisions. As inputs, they use a combination of literature information on the accuracy and precision of pathology and tests as well as expert judgment. Sensitivity and value-of-information analyses are conducted to identify important uncertainties. RESULTS AND CONCLUSIONS: The authors find a small but nontrivial probability that breast cancer patients will be incorrectly staged and thus may be subjected to inappropriate treatment. Results are sensitive to a number of variables, and some routinely used tests for metastasis have very limited information value. This work has implications for the methods used in cancer staging, and the methods are generalizable for quantitative risk assessment of treatment errors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it