Principles, effectiveness and caveats in screening for cancer
Bibliographic record
Abstract
BACKGROUND: Cancer screening has the potential to prevent or reduce incidence and mortality of the target disease, but may also be harmful and have unwanted side-effects. METHODS: This review explains the basic principles of cancer screening, common pitfalls in evaluation of effectiveness and harms of screening, and summarizes the evidence for effects and harms of the most commonly used cancer screening tools. RESULTS: Cancer screening has either been established or is considered for breast, lung, prostate, cervical and colorectal cancer. In contrast, screening for gastrointestinal malignancies outside the large bowel is not generally accepted, available or implemented. Oesophageal and gastric carcinoma, and hepatocellular carcinoma, may be subject to screening in certain risk populations, but currently not for population screening based on available technology. Screening for colorectal cancer and cervical cancer by endoscopy and cytology respectively can decrease incidence of the target disease, whereas screening tools for lung, prostate and breast cancer detect early-stage invasive disease and thus do not decrease disease incidence. Overdiagnosis (detection of cancers that will not have become clinically apparent in the absence of screening) is a challenge in lung, prostate and breast cancer screening. The improvement of quality of clinical practice following the introduction of cancer screening programmes is an appreciated 'side-effect', but it is important to disentangle the effect of screening on cancer incidence and mortality from that of quality improvement of clinical services. As new, powerful screening tests emerge-particularly in molecular and genetic fields, but also in radiology and other clinical diagnostics-the basic requirements for screening evaluation and implementation must be borne in mind. CONCLUSION: Cancer screening has been established for several cancer forms in Europe. The potential for incidence and mortality reduction is good, but harms do exist that need to be addressed, and communicated to the public.
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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 | 0.191 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.059 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".