Bibliographic record
Abstract
AIM: To report an analysis of the concept of active surveillance. BACKGROUND: Prostate cancer has become more prevalent since the introduction of PSA screening, however, many men are diagnosed with low-risk disease that may not require treatment. Active surveillance is a treatment strategy used to avoid treatment and related adverse effects when immediate treatment is not necessary. A universal definition is lacking. DESIGN: Concept analysis. DATA SOURCES: The CINAHL, PubMed, Scopus, Cochrane Library and Google Scholar databases were searched for literature published between 1980 and 2014 using the term active surveillance. METHODS: The method of Walker and Avant (2010) was used to analyse the concept of active surveillance, specifically within the context of prostate cancer. RESULTS: Key attributes of active surveillance emerging from the analysis include: regular and purposeful monitoring, early detection of disease progression and planned curative intervention if necessary. Multiple terms are used in the literature to refer to the concept of active surveillance. Active surveillance can cause uncertainty, and prompt men to make lifestyle changes and seek more information on prostate cancer. CONCLUSION: Active surveillance is not well understood, and ambiguity remains around the concept. Active surveillance and watchful waiting are used interchangeably in the literature and in clinical practice, but in fact do not refer to the same strategy. Active surveillance can generate significant uncertainty for the patient and family, which may be a barrier to choosing it as a treatment strategy and nursing research in this area is limited. RELEVANCE TO CLINICAL PRACTICE: Nurses need a clear understanding of active surveillance and how it differs from other strategies in order to reduce ambiguity around the concept. Nurses must be aware of the uncertainty accompanying active surveillance, and a need exists for continued nursing research in this area.
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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".