Bibliographic record
Abstract
For urological tumours, positron emission tomography (PET) is currently most useful in testicular cancer. In patients with residual masses or raised marker levels after treatment, PET is both sensitive and specific for detecting recurrent disease, at suspected and unsuspected sites. Although fewer studies are available it also appears to be useful for staging at diagnosis, although this requires further investigation. Prostate cancer imaging has been more variable, with studies showing that PET cannot reliably differentiate between tumour and hypertrophy. It is not as good as a bone scan for defining bone metastases. In renal cancer, PET can be used to define the primary tumour, providing better staging of local recurrence than computed tomography (CT), and to define metastatic disease. There are few studies in bladder cancer, and despite excretion of the tracer via the bladder in early studies, it has better results than CT or magnetic resonance imaging for local staging; again it can detect metastases. Overall, the place of PET in urological tumours is developing, with the strongest areas undoubtedly being testicular and renal cancer. Tracers other than fluorodeoxyglucose are being examined and are providing further information.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".