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
PURPOSE OF REVIEW: This is a summary of the current approach to patient selection for active surveillance, including eligibility criteria, current controversies and the role of imaging. RECENT FINDINGS: Active surveillance is based on the concept that Gleason 6 prostate cancer is, in most cases, an indolent condition that poses little or no threat to the patient's life. Substantial recent data suggest that Gleason pattern 3 does not have the molecular characteristics of malignancy. A subset of patients harbour more aggressive disease that was missed on the initial diagnostic biopsies, and a smaller group will progress over time to higher grade disease. Active surveillance involves initial expectant management for patients with favourable risk disease, and serial biopsy and prostate-specific antigen (PSA). Most patients with Gleason 6 prostate cancer are candidates. Very low risk patients fulfil the Epstein criteria, with only one or two positive cores, no core with more than 50% involvement and a PSA density of less than 0.15. Low-risk patients have Gleason 6 disease and PSA 10 or less but do not satisfy the Epstein criteria. Higher volume of Gleason 6 disease on biopsy predicts for a higher likelihood of higher grade cancer, but in and of itself should not mandate treatment. Patients with Gleason 7 in whom the extent of Gleason 4 pattern is less than 10% may also be candidates. Patient age, comorbidity and personal preferences must also be considered. SUMMARY: Active surveillance is an effective and well tolerated method to reduce the overtreatment associated with screen-detected prostate cancer. About 50% of newly diagnosed patients are eligible for this approach. Multiple factors, including patient age, comorbidity, cancer risk category and patient preferences, must be considered.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 it