Invited Commentary: Screening and the Elusive Etiology of Prostate Cancer
Bibliographic record
Abstract
The role of lifestyle risk factors in prostate cancer risk remains elusive despite a large number of epidemiologic studies. In a pooled analysis of data from South and East Asian countries published in this issue, Fowke et al. (Am J Epidemiol. 2015;182(5):381-389) found no evidence for an association between prostate cancer mortality and obesity, alcohol, or smoking. Prostate cancer screening is very uncommon in these countries, and previous evidence for associations with lifestyle factors comes primarily from studies carried out in North America, where screening is very common. Fowke et al. concluded that screening biases are likely to explain the differences in study results. In this commentary, we discuss the potential influence of population-based cancer screening programs in estimates of association from epidemiologic studies. This highlights the importance of carefully considering the impact of screening in the analysis and interpretation of results, in order to advance our understanding of the etiology of cancers that can be detected by screening.
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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.041 | 0.033 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".