Eidtorial from Editor-in-Chief (Breast Cancer: Still a Pending Issue where Much Effort is Needed)
Bibliographic record
Abstract
As Dr Russo and Dr Federico mentioned in their Editorial of this issue, breast cancer is the most common malignant tumor among women with approximately one million new cases per year worldwide. Disparities on the incidence of breast cancer among the rich and poor countries are still not fully understood. As Dr Awadelkarim and colleagues mentioned in their article, breast cancer incidence is lower in middle and low income countries, however, the majority of patients in Sub-Saharan countries present with locally advanced and metastatic disease. This lower incidence means that screening programs aimed at early detection in asymptomatic women would have a lower yield — i.e., substantially more women would need to be examined to find a true case of breast cancer [1]. Because the average age of breast cancer is generally lower in low-and-middle income countries (LMCs), it has been suggested that breast-cancer screening programs begin at an earlier age in these settings. However, the younger average age of breast cancer is mainly driven by the age distribution of the population, and the fact that there are fewer older women with breast cancer, rather than by higher age-specific incidence rates in younger women [1]. The hard work put in by the guest editors of this issue should be commended. They are looking for innovative and early markers of breast cancer which is of great importance particularly with the current controversies surrounding the use of mammography. The Canadian task force recently reported that women under 50 years of age at average risk should not have a mammogram; instead, mammography should be reserved only for women aged between 50 and 74 [2]. According to the Canadian experts, women should not receive clinical breast examinations or perform self-examinations at any age. The recommendations mirror those from the US Preventive Services Taskforce: in women who arent at high risk, dont start routine mammography until age 50, screen only every two to three years, and stop routine clinical breast examination. The fact that both these national task forces have reached the same conclusion should give some confidence in the result, but there is no doubting the controversy. Nor are we dealing with a stationary target: new evidence is emerging all the time [3]. This is a new example of the need to gather new evidence through well-designed and well-performed research, along with the systematic review of the literature and the compilation of this evidence as a crucial methodology in guiding providers and consumers on the best practices, avoiding useless practices and to informing users on the expectations of using procedures and interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".