Bibliographic record
Abstract
A theme for this issue is data quality. Wehbi and colleagues 1 review the progress made in randomized clinical trials (RCTs) over the last few decades, particularly in prostate cancer. RCTs have increased, although they still represent a small minority of published studies. Encouragingly, almost 50% of the RCTs were led by urologists, and 7% of the RCTs worldwide were from Canada. The authors conclude with a plea for greater efforts to initiate and complete RCTs. This is laudable. The authors rightly emphasize that a randomization of patients reduces the confounding effects of selection bias. But, despite high hopes for specific initiatives, RCTs (more often than not) fail to answer the question which motivated the original trial. Biology is complex, and gives up its secrets reluctantly. Difficulties of trial design, implementation, patient and disease heterogeneity, changing epidemiology and evolving science confound the best of intentions. For example, the mother of all prostate trials, ERSPC, has not resolved the question of whether screening is warranted (although it has served to focus the discussion and clarify the issues). PLCO tried to answer the same question, but arguably the results have only confused matters. PCPT and REDUCE do not appear to have resolved the question of the preventive benefit of 5-ARIs. Randomized trials of androgen deprivation therapy have left many key questions unanswered about timing of therapy, combination therapy versus monotherapy, the importance of nadir testosterone, etc. The phenomenon, whereby a supposedly definitive trial mainly serves to raise more questions, can be observed in all urology subspecialities, and indeed throughout medicine.
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 | 0.712 | 0.879 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.032 | 0.063 |
| Open science | 0.017 | 0.022 |
| Research integrity | 0.026 | 0.043 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".