What does cancer screening have to do with tomato growing?
Bibliographic record
Abstract
Cancer screening is considered to be a major strategy for combatting cancer. The United States Preventive Services Task Force (USPSTF) recommends screening for five cancers, but the strength of evidence about the effectiveness of screening is limited. To gain insights into the efficacy of early detection requires prospective, blinded, placebo-controlled clinical trials with decades of follow-up and inclusion of millions of participants. Recently, Bretthauer et al. estimated lifetime gained with cancer screening tests by using a meta-analysis of 18 large randomized clinical trials which included more than two million subjects. They asked if cancer screening tests are saving lives and how much life is extended due to commonly used cancer screening tests. Colorectal cancer screening with sigmoidoscopy prolonged lifetime by 110 days, while fecal testing and mammography screening did not prolong life. A modest extension of 37 days was noted for prostate cancer screening with prostate-specific antigen testing and 107 days with lung cancer screening using computed tomography, but these estimates were not statistically significant. The authors concluded that current cancer screening strategies do not significantly prolong life. Based on these data, and the known biological behavior of some cancers, we hypothesized that the current strategies of treating cancer, after detection, could be modified to avoid the side effects of screening, which is a major determinant of the patient's overall survival.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".