Active surveillance for early‐stage prostate cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The natural history of prostate cancer is remarkably heterogeneous and, at this time, not completely understood. The widespread adoption and application of prostate-specific antigen (PSA) screening has led to a dramatic shift toward the diagnosis of low-volume, nonpalpable, early-stage tumors. Autopsy and early observational studies have shown that approximately 1 in 3 men aged >50 years has histologic evidence of prostate cancer, with a significant portion of tumors being small and possibly clinically insignificant. Utilizing the power of improved contemporary risk stratification schema to better identify patients with a low risk of cancer progression, several centers are gaining considerable experience with active surveillance and delayed, selective, and curative therapy. A literature review was performed to evaluate the rationale behind active surveillance for prostate cancer and to describe the early experiences from surveillance protocols. It appears that a limited number of men on active surveillance have required treatment, with the majority of such men having good outcomes after delayed selective intervention for progressive disease. The best candidates for active surveillance are being defined, as are predictors of active treatment. The psychosocial ramifications of surveillance for prostate cancer can be profound and future needs and unmet goals will be discussed.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 |
| 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 it