Predicting Breast Cancer Mortality in the Presence of Competing Risks Using Smartphone Application Development Software
Bibliographic record
Abstract
The widespread use of smartphone applications (apps) provides a promising new platform for medical research and healthcare decision making. Given the need to help guide clinical discussions about the appropriateness of breast cancer screening in the presence of competing risks among older women, we proposed to incorporate the Fine-Gray prediction model, which offers more intuitive clinical interpretation of risk in the presence of competing risks, into a smartphone-based decision aid application. Clinicians can input the woman’s characteristics and medical history, and the app will output prediction estimates of both types of events (i.e. death from breast cancer and competing risk events) given the presence or absence of breast cancer screening. This prototype was built using drag-and-drop visual programming tools provided by the free, cloud-based software “MIT App Inventor for Android.” It will be intended for clinicians to use in the context of patients’ values to decide whether screening is appropriate for an individual. Our analysis indicated that screening was beneficial to survival, and that older women benefited less from screening due to the increasing incidence of non-breast-cancer competing risk deaths as age increased. The algorithm we implemented for the app provides instant probability estimates that help quantify screening benefits as a function of age, and comorbidity burden.
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How this classification was reachedexpand
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".