Eligibility criteria requirements and adherence in advanced prostate cancer trials.
Bibliographic record
Abstract
245 Background: Eligibility criteria for advanced (metastatic and locally advanced) prostate cancer trials vary between trials and are specific to the therapy evaluated, and may not represent the general population. This reduces comparability between studies, generalizability of results, and may contribute to high screen failure rates (~25%). In this study, we aimed to better understand the eligibility criteria across trials and adherence to eligibility criteria. Methods: We compared eligibility criteria between trials and collected baseline demographics to assess for adherence to criteria for 13 phase III advanced prostate cancer trials published from 1999-2016. Results: Disease characteristics, age, performance status, tumor markers, and most hepatic, renal, and hematological criteria were consistent across trials. However, discrepancies in hemoglobin, absolute neutrophil count, glomerular filtration rate (GFR) and bilirubin criteria were observed (Table). Overall, 8/13 trials included patients that did not meet criteria but were analyzed by intent-to-treat. Criteria that were not followed included: hemoglobin and albumin (lower than permitted); while testosterone, serum creatinine, pain scores, and ECOG performance status were higher (more unwell) than permitted. Conclusions: Specific eligibility criteria vary across the trials, and a number of trials accrued ineligible patients. After accounting for drug-specific criteria, greater standardization of and consideration of general population for eligibility criteria is needed to improve homogeneity of trial populations across studies, generalizability of results, and possibly reduce screen failure rates in prostate cancer trials. [Table: see text]
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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 | 0.505 | 0.670 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".