Active surveillance review: contemporary selection criteria, follow-up, compliance and outcomes
Bibliographic record
Abstract
The primary goal of active surveillance (AS) is to prevent overtreatment by selecting patients with low-risk prostate cancer (PCa) and closely monitoring them so that definitive treatment can be offered when needed. With the increasing popularity of AS as a management strategy for men with localized PCa, it is important to understand all the contemporary guidelines and criteria that exist for AS and the differences among them. No single optimal management strategy for clinically localized, early-stage disease has been universally accepted. The implementation of AS varies widely between institutions, from inclusion criteria to follow-up protocols, with the most notable differences seen in maximum accepted Gleason score, T-stage and prostate-specific antigen (PSA) parameters. The objectives of this review were to systematically summarize the current literature on AS strategy, present an overview of the various published guidelines and criteria that are used for AS at several major institutions as well as discuss goals and trade-offs of the various criteria. A comprehensive search of the PubMed and Embase databases from 1990 to 2017 was performed to identify studies pertaining to AS criteria and trends. Trends in AS uptake and use in Canada, USA and Europe were reviewed to demonstrate the current trends and outcomes of AS to offer greater insight into the differences, nature and efficacy of various AS protocols. AS is a compelling antidote to the current PCa overtreatment phenomena; however, when considering patients for AS it is important to understand the differences between protocols, and review published results to appreciate the impact on follow-up.
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 | 0.018 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".