Recommendations for a step‐wise comparative approach to the evaluation of new screening tests for colorectal cancer
Bibliographic record
Abstract
BACKGROUND: New screening tests for colorectal cancer continue to emerge, but the evidence needed to justify their adoption in screening programs remains uncertain. METHODS: A review of the literature and a consensus approach by experts was undertaken to provide practical guidance on how to compare new screening tests with proven screening tests. RESULTS: Findings and recommendations from the review included the following: Adoption of a new screening test requires evidence of effectiveness relative to a proven comparator test. Clinical accuracy supported by programmatic population evaluation in the screening context on an intention-to-screen basis, including acceptability, is essential. Cancer-specific mortality is not essential as an endpoint provided that the mortality benefit of the comparator has been demonstrated and that the biologic basis of detection is similar. Effectiveness of the guaiac-based fecal occult blood test provides the minimum standard to be achieved by a new test. A 4-phase evaluation is recommended. An initial retrospective evaluation in cancer cases and controls (Phase 1) is followed by a prospective evaluation of performance across the continuum of neoplastic lesions (Phase 2). Phase 3 follows the demonstration of adequate accuracy in these 2 prescreening phases and addresses programmatic outcomes at 1 screening round on an intention-to-screen basis. Phase 4 involves more comprehensive evaluation of ongoing screening over multiple rounds. Key information is provided from the following parameters: the test positivity rate in a screening population, the true-positive and false-positive rates, and the number needed to colonoscope to detect a target lesion. CONCLUSIONS: New screening tests can be evaluated efficiently by this stepwise comparative approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.282 | 0.573 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.018 | 0.006 |
| Research integrity | 0.025 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".