Perfect is the Enemy of Good: Going to the War on Cancer with Less Evidence than We Could Have
Bibliographic record
Abstract
Abstract Randomized controlled trials have been the mainstay of high-level evidence for or against cancer screening strategies. However, simple proof of reduction in cause-specific mortality is not enough for policymaking, particularly in the last 10 years. Today’s clinical and public health guidelines take into account the balance of risks to benefits from screening, costs, utilities, the political risk of inaction, the societal tolerance to risk, healthcare providers’ preferences, client choices, and other imponderables or subjective variables that cannot be captured or addressed via epidemiologic studies. This commentary uses the above perspective in discussing Miettinen’s arguments concerning the science of breast cancer 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 | 0.121 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.024 | 0.041 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".