CLOVIZ: Clinical outcomes visualization of IMDC criteria in metastatic renal cell carcinoma for patient-centered decision making.
Bibliographic record
Abstract
527 Background: The International Metastatic Renal Cell Carcinoma Database (IMDC) Criteria (Heng Criteria) is a validated risk prediction tool for patients with metastatic renal cell carcinoma (mRCC). It provides valuable prognostic data but clinical application can be challenging due to limited available tools. We created an interactive visualization to facilitate clinical application of IMDC Criteria. Methods: A multi-institutional cohort of 436 patients with mRCC was used to create an interactive visualization depicting IMDC Criteria at the patient level. Usability testing was performed with non-medical lay-users and medical oncology fellows. Subjects used the tool to calculate median survival times based on IMDC Criteria in six increasingly complex clinical scenarios. Confidence using the tool was surveyed and measured along a 5-point Likert scale. Results: The interactive visualization is available at http://faculty.washington.edu/odisho . 400 lay-users and 15 medical oncology fellows completed clinical scenarios and surveys. Overall, lay-users were able to obtain the exact correct answer in 48% of scenarios, compared to 60% of medical oncology fellows. The proportion of exact correct answers decreased with increasing task complexity, but the proportion of answers within 25% of the expected answer remained stable at 68-78% for lay-users and 73-93% for medical oncology fellows. When surveying usability, 65% of lay-users felt it was easy to use, compared to 80% of fellows, and 83%-87% felt it became intuitive with increasing use. Among lay-users, 69-77% were confident selecting lab values and drugs, compared to 87-93% of medical oncology fellows. 75% of lay-users felt it helped them better understand survival in mRCC. 68% of lay-users wanted to use a similar tool with their doctor, while 87% of medical oncologists wanted to use this with patients, and 93% wanted to incorporate it into their clinical practice in some way. Conclusions: A graphical method of interacting with a validated nomogram for mRCC outcomes provides real-time individual level data that can be used by untrained nonmedical users and medical oncologists, with potential for use in the clinic setting.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".