S650 AI-Assisted vs Traditional Colonoscopy in Colorectal Neoplasia Detection: A Systematic Review and Meta-Analysis of RCTs and Observational Studies
Bibliographic record
Abstract
Introduction: Over 80% of people who smoke nicotine are not up-to-date with lung cancer screening, and more than 40% of eligible adults are not current with colorectal cancer (CRC) screening. Blood-based tests for both cancers offer a promising alternative to existing options. In this study, we used conjoint analysis to assess preferences for blood-based lung and CRC screening tests compared to traditional methods (e.g., low-dose CT [LDCT] for lung cancer; fecal immunochemical test [FIT], multitarget stool DNA [mt-sDNA], and colonoscopy for CRC). Methods: We conducted a choice-based conjoint survey of US adults aged 50–75 years who were eligible but not up-to-date for lung and CRC screening per USPSTF guidelines. In the conjoint exercises, participants evaluated side-by-side hypothetical lung cancer and CRC screening test combinations that varied by modality, frequency, accuracy, and other features. We used hierarchical Bayes regression to estimate individual part-worth utilities, which were then used to conduct simulations assessing respondents’ preferred lung cancer and CRC screening test combinations: blood tests for both cancers, blood test for one and traditional for the other, or traditional tests for both. Multivariable logistic regression adjusted for confounding and identified factors associated with preferring blood-based screening for both cancers. Results: A total of 1,741 participants completed the survey; 43.9% of respondents preferred to do blood tests every 3 (Q3) years for both cancers, while 28.6% favored traditional options. Another 21.9% preferred a lung cancer blood test Q3 years with a traditional CRC test; 5.6% chose annual lung LDCT with a CRC blood test Q3 years. Regression results showed that non-married individuals, those with comorbidities, and those who do not exercise regularly were more likely to prefer blood tests to screen for both cancers (all P < 0.05). In contrast, non-Hispanic Black individuals with higher income, and those with a usual source of care were less likely to choose blood testing (all P < 0.05). Conclusion: Among a national sample of individuals not up-to-date with lung and CRC screening, nearly half preferred blood tests for both cancers over traditional methods. These findings suggest that blood-based screening is broadly acceptable and may help address barriers to participation in cancer screening programs.
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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 | 0.030 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".