An international consensus on the essential and desirable criteria for an ‘organized’ cancer screening programme
Bibliographic record
Abstract
BACKGROUND: High variability in the definition and interpretation of organized cancer screening needs to be addressed systematically. Moreover, the relevance of the current practice of categorizing screening programmes dichotomously into organized or non-organized needs to be revisited in the context of considerable heterogeneity that exists in the delivery of cancer screening in the real world. We aimed to identify the essential and desirable criteria for organized cancer screening that serve as a charter of best practices in cancer screening. METHODS: We first did a systematic review of literature to arrive at an exhaustive list of criteria used by various publications to describe or define organized cancer screening, based on which, a consolidated list of criteria was generated. Next, we used a Delphi process comprising of two rounds of online surveys to seek agreement of experts to categorize each criterion into essential, desirable, or neither. Consensus was considered to have been achieved based on a predetermined criterion of agreement from at least 80% of the experts. The outcomes were presented before the experts in a virtual meeting for feedbacks and clarifications. RESULTS: A total of 32 consolidated criteria for an organized screening programme were identified and presented to 24 experts from 20 countries to select the essential criteria in the Delphi first round. Total 16 criteria were selected as essential with the topmost criteria (based on the agreement of 96% of experts) being the availability of a protocol/guideline describing at least the target population, screening intervals, screening tests, referral pathway, management of positive cases and a system being in place to identify the eligible populations. In the second round of Delphi, the experts selected eight desirable criteria out of the rest 16. The most agreed upon desirable criterion was existence of a specified organization or a team responsible for programme implementation and/or coordination. CONCLUSIONS: We established an international consensus on essential and desirable criteria, which screening programmes would aspire to fulfil to be better-organized. The harmonized criteria are a ready-to-use guide for programme managers and policymakers to prioritize interventions and resources rather than supporting the dichotomous and simplistic approach of categorizing programmes as organized or non-organized.
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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 | 0.299 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".