Bibliographic record
Abstract
The word "cancer" invokes fear even today in those diagnosed with it, largely due to the deep-rooted stigma associated with this emotive word, one associated with an incurable and fatal disease. This was true in years gone by, when cancer patients presented late with symptoms from advanced disease. Today, however, in the era of screening and an awareness of the value of early detection, it is no longer the case. The last half century has heralded an unparalleled rise in every aspect of cancer research, diagnostics and therapeutics, with a better understanding of basic science, pathological classifications, risk factors, prognosis and treatments. Screening programs have been adopted or suggested for many cancers. The pendulum is shifting. A new concept has emerged - that of cancer overdiagnosis, and together with this, cancer overtreatment. Medicine still remains a science of uncertainty and an art of assessing probability. Until personalized medicine evolves to a level that a person's lifetime risk of clinically significant cancer formation and expected outcome can be computed with a great degree of precision and confidence, clinicians and patients have to be cognizant of the problem of cancer overdiagnosis and overtreatment. In this editorial, we explore the current evidence and magnitude of this problem.
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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".