Bibliographic record
Abstract
List of contributors. Part 1: Biological and Scientific Aspects. 1 Tim Lane, Jon Strefford and Tim Oliver. An Update on Biotechnology in the Assessment of Prostate Cancer. 2 Ludger Barthelmes and Christopher A. Gateley. What Can We Learn From Breast Cancer?. 3 Bernhard Brehmer, Antonio Smakris and Gerhard Jakse. New Serum Markers for Prostate Cancer. 4 Kenichiro Ishii and Simon Hayward. The History of Tissue Recombination Technology: Current and Future Research. 5 Malcolm Mason and John Staffurth. Why We Cannot Use the Results of Non-Randomised Trials to Inform Us About Treatment for Early Prostate Cancer?. Part 2: Diagnosis and Evaluation. 6 Bob Djavan, Michael Dobrovits and Michael Marberger. Equivocal PSAResults and Free Total PSARatio. 7 Murali Varma And David Griffiths . Equivocal Prostate Needle Biopsies. 8 Richard Clements. Prostate Biopsy: How Many Cores and Where From?. 9 Gail Beese and Christopher Edmunds. Counselling Patients With Early Prostate Cancer. 10 Owen Niall and Jamie Kearsley. The Role of Pelvic Node Dissection in Prostate Cancer. Part 3: Initial Treatment Policies. 11 Mark Wright. Laparoscopic Radical Prostatectomy. 12 A. Goyal and W. Bowsher. Endocrine Therapy for Prostate Cancer: the Latest. 13 Stijn De Vries, Christopher Bangma and Fritz Schroder. The Role of Conservative Policies in the Treatment of Prostate Cancer. 14 Gary Deng and Barrie Cassileth. Complementary and Alternative Therapies for Prostate Cancer. Part 4: Monitoring Progress and SecondaryTreatment. 15 Amir Kaisary. Radical Prostatectomy After Radical Radiotherapy. 16 Paul Jones and Neil Fenn. Treatment of Renal Impairment Secondary to Locally Advanced Prostate Cancer. 17 Robert P. Myers, R. Houston Thompson, Stephen M. Schatz and Michael L. Blute. Open Radical Prostatectomy: How Can Intra-operative, Peri-operative and Post-operative Complications Be Prevented?. 18 Leslie Moffat. Documenting Prostate Cancer: Epidemiology and Treatment. 19 Steven Oliver, Rhidian Hurle and Owen Hughes. Trends in Prostate Cancer Incidence and Mortality. 20 Jason Lester and E M Mahudson. Chemotherapy in Prostate Cancer. Index. Colour plates
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.079 | 0.043 |
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".