Are cancer screening recommendations from top US cancer centers concordant with the USPSTF guidelines?
Bibliographic record
Abstract
10535 Background: The USPSTF is an independent volunteer organization of multi-disciplinary national experts in disease prevention that publishes evidence-based non-binding recommendations for screening diseases, including cancer. Herein we assessed the congruence of cancer screening recommendations between the USPSTF and the Top 10 US Cancer Centers (as per the U.S News and World Report). Methods: This cross-sectional study compared the published screening recommendations from the Top 10 US Cancer Centers for colorectal (CRC), lung (LC), cervical (CC), prostate (PC), and breast cancers (BC) in average risk patients, to those of the USPSTF. The variables of interest included: screening tests, recommendation direction (for/against), strength, frequency, age of start, age of end, and discussion about risks and benefits. Data extraction was done by 2 authors and discrepancies resolved by mutual consensus and discussion with a third author. Results: We demonstrate the differences between the screening recommendations from the Top 10 Cancer Centers relative to those from the USPSTF (Table). Conclusions: Our study found significant variability between the screening recommendations from the 2022-2023 USNWR top 10 cancer centers and the evidence-based USPSTF guidelines for BC, PC, CC, and CRC. The recommendations for LC were overall in line with those of the USPSTF. The discordance was almost always in the direction of the cancer centers recommending more screening beyond the USPSTF’s recommendations, and often without discussion of potential risks and harms. This inconsistency could create confusion to the public, and therefore, a consistent messaging across the cancer centers congruent with the USPSTF recommendations and including a nuanced discussion of benefits and risks may be in the best public health interest.[Table: see text]
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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 | 0.109 | 0.441 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".