U.S. Preventive Services Task Force (USPSTF) recommendations on breast cancer screening (BCS): Are they justified?
Bibliographic record
Abstract
1584 Background: Nine randomized population trials (RPTs) on BCS with mammography have been reported. Conclusions based upon mortality (MOR) comparisons in RPTs have long led to uncertainty about whether BCS saves lives, especially in women in their 40s. In Nov 2009, USPSTF recommended against BCS in women 40-49 and biennial BCS for women >50. By 2010, BCS fell by >4% in US. Does the evidence support USPSTF recommendations? Methods: MOR quantifies the effect of intervention across an entire population. However, the key issue is whether BCS reduces MOR among those with disease. This is reflected by survival (SUR), not MOR. While SUR is considered flawed due to conventional screening biases, the confounding influence of these biases is misunderstood. In a RPT, randomization provides an opportunity to eliminate these biases, so that (SUR) may accurately reflect screening efficacy. MOR would be biased if randomization fails to produce populations at equal risk for the target disease. Results: Among 9 RPTs, significant MOR reductions were reported only in Swedish Two-County Study in women >50 and in the Gothenburg Study in women 39-49. However, problems with randomization were responsible for MOR overestimating BCS efficacy in these trials. In 7 RPTs, significant MOR reductions were not seen. However, MOR underestimated BCS efficacy in several RPTs. The Canadian National BCS Study, the most influential RPT in women <50 yrs, showed a trend toward increased BC MOR in the screened group. However, randomization failure was likely responsible for significantly more high risk BC in the screening group, which confounded MOR comparisons. Conclusions: It’s never been possible to justify BCS based on MOR comparisons in RPTs. While meta-analyses have been widely utilized, they can be biased when MOR fails to accurately reflect BCS in individual RPTs. Abundant evidence from RPTs supports that BCS leads to significant stage and SUR advantages not attributable to conventional biases, and which more accurately reflect BCS efficacy. The magnitude of benefit is similar for women in their 40s and for those >50. USPSTF recommendations are not supported by the data. If fully implemented, these guidelines can lead to increased BC MOR in US.
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 | 0.102 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".