Breast cancer causes and treatment: where are we going wrong?
Bibliographic record
Abstract
This discussion paper seeks to provoke thoughts about cancer research in general, and why breast cancer in particular is not yet "curable". It asks the question - are we looking at the disease in the right way? Should we regard cancer as a progressive state, which is part of aging? Should we tailor treatment to "reset" the system or slow progression rather than try using toxic and aggressive therapy to kill every cancer cell (and sometimes also the patient)? The thesis is presented that we need to revisit our fundamental beliefs about the disease and then ask why we cling to beliefs that clearly are no longer valid. The paper also questions the role of ethics boards in hampering research and discusses the concept that breast cancer is an industry with vested interests involving profiteering by preventive, diagnostic, and therapeutic players. Finally, the paper suggests some ways forward based on emerging concepts in system biology and epigenetics.
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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 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".