Bibliographic record
Abstract
Active surveillance for favorable risk prostate cancer has become increasingly popular in populations where prostate cancer screening is widespread, because of evidence that prostate cancer screening results in the detection of disease that is not clinically significant in many patients (i.e., untreated, would not pose a threat to health). This approach is supported by data showing that patients who fall into the category of clinically insignificant disease can be identified with reasonable accuracy, and that patients who are initially classified as low-risk who reclassify over time as higher-risk and are treated radically are still cured in most cases. Active surveillance means 1) identifying patients who have a low likelihood of disease progression during their lifetime, based on clinical and pathologic features of the disease, and patient age and comorbidity; 2) close monitoring over time; 3) developing reasonable criteria for intervention, which will identify more aggressive disease in a timely fashion and not result in excessive treatment; and 4) meeting the communication challenge to reduce the psychological burden of living with untreated cancer. This article reviews the results of active surveillance, the criteria for patient selection, and the appropriate triggers for intervention.
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 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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".