Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration
Bibliographic record
Abstract
<ns4:p>In today's information age, the necessary means exist for clinical risk prediction to capitalize on a multitude of data sources, increasing the potential for greater accuracy and improved patient care. Towards this objective, the Prostate Cancer DREAM Challenge posted comprehensive information from three clinical trials recording survival for patients with metastatic castration-resistant prostate cancer treated with first-line docetaxel. A subset of an independent clinical trial was used for interim evaluation of model submissions, providing critical feedback to participating teams for tailoring their models to the desired target. Final submitted models were evaluated and ranked on the independent clinical trial. Our team, called "A Bavarian Dream", utilized many of the common statistical methods for data dimension reduction and summarization during the trial. Three general modeling principles emerged that were deemed helpful for building accurate risk prediction tools and ending up among the winning teams of both sub-challenges. These principles included: first, good data, encompassing the collection of important variables and imputation of missing data; second, wisdom of the crowd, extending beyond the usual model ensemble notion to the inclusion of experts on specific risk ranges; and third, recalibration, entailing transfer learning to the target source. In this study, we illustrate the application and impact of these principles applied to data from the Prostate Cancer DREAM Challenge.</ns4:p>
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.
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 | 0.012 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".