Questioning the benefits of current screening protocols for common cancers
Bibliographic record
Abstract
Screening is the application of a diagnostic test to a population of patients without suspicious clinical signs or symptoms of a disease. Applying screening protocols to detect cancer is especially attractive as cancer is a growing burden and earlier detection of malignancies can lead to better outcomes. While the initial excitement over screening for cancers has caused protocols to be put into practice, data analysis has cast doubt on the net benefit. Prostate-specific antigen (PSA) blood testing has been used as a screening test for prostate cancer. The harms of a positive PSA screen include invasive investigations and treatments with side effects such as infection and incontinence. The poor sensitivity and specificity of PSA screening caused the Canadian Task Force on Preventive Health Care to recommend against PSA as a screening tool. Similarly, mammography and ultrasound for detecting breast cancer lesions in asymptomatic women suffer from relatively low sensitivity and specificity. The harms of screening include emotional stress and invasive surgical procedures. Meta-analyses reveal that the risks outweigh the benefits for some age groups, but there may be a role in screening asymptomatic patients with a family history of breast cancer with more sensitive but more expensive modalities, such as magnetic resonance imaging (MRI). Finally, colonoscopy, which is the gold standard to diagnose colorectal cancers, has failed to show net benefit in screening. While screening protocols thus far have provided underwhelming results, the key in screening in the future may lie in using tests with high specificity and sensitivity, as in next generation screening modalities that rely on molecular markers and analytics.
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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 | 0.297 | 0.685 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".