Interactive Data Visualization Tool for Patient-Centered Decision Making in Kidney Cancer
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
PURPOSE Patients and providers often lack clinical decision tools to enable effective shared decision making. This is especially true in the rapidly changing therapeutic landscape of metastatic kidney cancer. Using the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria, a validated risk prediction tool for patients with metastatic renal cell carcinoma, we created and user-tested a novel interactive visualization for clinical use. METHODS An interactive visualization depicting IMDC criteria was created, with the final version including data for more than 4,500 patients. Usability testing was performed with nonmedical lay-users and medical oncology fellow physicians. Subjects used the tool to calculate median survival times based on IMDC criteria. User confidence was surveyed. An iterative user feedback implementation cycle was completed and informed revision of the tool. RESULTS The tool is available at CloViz—IMDC. Initially, 400 lay-users and 15 physicians completed clinical scenarios and surveys. Cumulative accuracy across scenarios was higher for physicians than lay-users (84% v 74%; P = .03). Eighty-three percent of lay-users and 87% of physicians thought the tool became intuitive with use. Sixty-eight percent of lay-users wanted to use the tool clinically compared with 87% of physicians. After revisions, the updated tool was user-tested with 100 lay-users and 15 physicians. Physicians, but not lay-users, showed significant improvement in accuracy in the updated version of the tool (90% v 67%; P = .008). Seventy-two percent of lay-users and 93% of physicians wanted to use the updated tool in a clinical setting. CONCLUSION A graphical method of interacting with a validated nomogram provides prognosis results that can be used by nonmedical lay-users and physicians, and has the potential for expanded use across many clinical conditions.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it