PROSPeCT: A Predictive Research Online System for Prostate Cancer Tasks
Bibliographic record
Abstract
PURPOSE: PROSPeCT), was developed to enable users to query the Alberta Prostate Cancer Registry database hosted by the Alberta Prostate Cancer Research Initiative. To deliver high-quality patient treatment, prostate cancer clinicians and researchers require a user-friendly system that offers an easy and efficient way to obtain relevant and accurate information about patients from a robust and expanding database. METHODS: PROSPeCT was designed and implemented to make it easy for users to query the prostate cancer patient database by creating, saving, and reusing simple and complex definitions. We describe its intuitive nature by exemplifying the creation and use of a complex definition to identify a "high-risk" patient cohort. RESULTS: PROSPeCT was made to minimize user error and to maximize efficiency without requiring the user to have programming skills. Thus, it provides tools that allow both novice and expert users to easily identify patient cohorts, manage individual patient care, perform Kaplan Meier estimates, plot aggregate PSA views, compute PSA-doubling time, and visualize results. CONCLUSION: This report provides an overview of PROSPeCT, a system that helps clinicians to identify appropriate patient treatments and researchers to develop prostate cancer hypotheses, with the overarching goal of improving the quality of life of patients with prostate cancer. We have made available the code for the PROSPeCT implementation at https://github.com/max-uhlich/e-PROSPeCT .
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.014 |
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".