How do cancer screening guidelines trade off benefits versus harms and burdens of screening? A systematic survey
Bibliographic record
Abstract
OBJECTIVES: Cancer screening guidelines differ in their recommendations for or against screening. To be able to provide explicit recommendations, guidelines need to specify thresholds for the magnitude of benefits of screening, given its harms and burdens. We evaluated how current cancer screening guidelines address the relative importance of benefits versus harms and burdens of screening. DATA SOURCE: We searched the Guidelines International Network, International Guideline Library, ECRI Institute and Medline. Two pairs of reviewers independently performed guideline selection and data abstraction. ELIGIBILITY CRITERIA: We included all cancer screening guidelines published in English between January 2014 and April 2019. RESULTS: Of 68 eligible guidelines, 25 included a statement regarding the trade-off between screening benefits versus harms and burdens (14 guidelines), or a statement of direction of the net effect (defined as benefits minus harms or burdens) (13 guidelines). None of these 25 guidelines defined how large a screening benefit should be to recommend screening, given its harms and burdens. 11 guidelines performed an economic evaluation of screening. Of these, six identified a key benefit outcome; two specified a cost-effectiveness threshold for recommending a screening option. Eight guidelines commented on people's values and preferences regarding the trade-off between benefits versus harms and burdens. CONCLUSIONS: Current cancer screening guidelines fail to specify the values and preferences underlying their recommendations. No guidelines provide a threshold at which they believe the benefits of screening outweigh its harms and burdens. PROSPERO REGISTRATION NUMBER: CRD42019138590.
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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 | 0.069 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".