LETTER TO THE EDITOR Response to: “Beyond the mammography debate: a moderate perspective” The Editor Current Oncology
Bibliographic record
Abstract
iklidis covers a lot of ground, and there is much to agree with. Certainly, we should be moving forward to find and evaluate better ways of detecting, characterizing, and treating breast (and other types of) cancer. In fact, I lead a large program at the Ontario Institute for Cancer Research focused on exactly those goals. But I must respectfully take issue with some of his statements. For example, he contends that “Only suffi-ciently powered randomized trials—if still feasible in this age... can hope to be more decisive on the central issues of a debate”1. Here, the “debate ” is about the value of mam-mography screening for breast cancer. From a scientific point of view, the debate should be over. For example, an editorial in The Lancet summarizing the report of the Independent UK Panel on Breast Cancer
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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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.029 | 0.040 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".