Project data sphere (PDS) in prostate cancer: A first look including concomitant medication use.
Bibliographic record
Abstract
204 Background: PDS enables patient-level analyses of control arms of cancer trials. The interface (www.projectdatasphere.org) allows for both web-based and download-based analyses. We aimed to validate established prostate cancer prognostic models and explore the effect of concomitant medications on survival in mCRPC. Methods: Data was obtained for 2,747 control subjects with mCRPC from 7 Phase III clinical trials with 1962 subjects available for OS analyses from 5 studies. Overall survival was estimated using the Kaplan-Meier Method. Cox-proportional hazards models, stratified by trial, were used to estimate hazard ratios. Results: Metastatic site was significant for overall survival (Median: Node only 23.69m, Bone 18.17m, Lung 14.72m, Liver 9.43m; p < 0.001). Of the 23 types of medication examined, after adjusting for metastatic site, patients taking proton pump inhibitors (HR: 1.155, p=0.017) and Erythropoietin (HR: 1.49, p-value<.001) had worse overall survival whilst patients taking fish oil (HR:0.68, p-value=0.033) and non-lipophilic statins (HR:0.69, p=.00277) had improved overall survival. Within the limits of available data, we validated the prognostic models for overall survival proposed by Templeton et al. and Sonpavde et al individually and after inclusion of concomitant medication where patients taking metformin (HR=0.729, p=.0082) and Cox 2 inhibitors (HR=0.708, p=.015) had improved OS whilst those taking low molecular weight heparin (HR=1.352, p=.004) had worse OS. Conclusions: As a first project utilizing open-source PDS data in prostate cancer, we validated two prostate cancer prognostic models and illuminated the ability to undertake novel analyses such as the association of concomitant medications with outcome. Limitations of the data relate to incomplete and inconsistent data entry. Future expansion of patient trials and numbers will help to facilitate future analyses.
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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 | 0.031 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".