Bibliographic record
Abstract
PURPOSE OF REVIEW: Overdiagnosis has become a major problem in medicine in general and cancer in particular. This is a summary of this problem. RECENT FINDINGS: Because of earlier detection, the nature of cancer has changed, from a disease usually diagnosed at a late and incurable stage to a heterogeneous condition that varies from clinically insignificant to rapidly aggressive. Screening programs for cancer have resulted in a dramatic increase in the diagnosis of clinically insignificant disease, balanced by improved survival and mortality because of significant cancers being diagnosed at a more curable stage. Overdiagnosis requires the presence of microfocal disease and a screening test to identify this. This exists for breast, prostate, and thyroid cancers, and to a lesser degree for renal and lung cancer. The problem of cancer overdiagnosis and overtreatment is complex, with numerous causes and many trade-offs. It is particularly important in prostate cancer, but is a major issue in many other cancer sites. Screening for prostate cancer appears, based on the best data from randomized trials, to significantly reduce cancer mortality. SUMMARY: Reducing overtreatment in patients diagnosed with indolent disease is critical to the success of screening.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".