Bibliographic record
Abstract
A few years ago, I attended the annual meeting of the Clinical Ligand Assay Society. From the airport, I took a taxi to my hotel. The taxi driver, a 60-year-old man, was curious and asked why I was visiting Philadelphia. I told him that I was attending a medical conference and giving a lecture on prostate cancer. He immediately got very excited! He showed me a 2-L Coca Cola® bottle, which was half full with a reddish fluid. He then asked me, “Do you know what this is?” I told him that I had never seen red Coca Cola and I wondered if it was a new product. He laughed and told me that only the bottle was from Coca Cola and that the content was watermelon juice. He mentioned drinking approximately 2 L per day, and when I asked why, he explained that somebody told him that drinking 2 L of watermelon juice per day could prevent the development of prostate cancer. He then told me that his PSA4 (prostate-specific antigen) was going down, and I was admittedly a bit ashamed that I did not know about this “new” chemopreventive agent. I used the story as an introduction to my lecture, and it seemed to have worked well with the audience. It is now 10 years later, and I am reviewing the recent literature on chemoprevention of prostate cancer with a new agent, dutasteride (1). This is not the first time that a chemical agent has been tried for prostate cancer prevention. The Prostate Cancer Prevention Trial (PCPT) tested finasteride with some apparently promising results (2) (see also below), and a Finnish study also examined finasteride (3). On the basis of these and other data, the American Society of Clinical Oncology and the American Urological Association issued a …
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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".