Effects of Delayed Radical Prostatectomy and Active Surveillance on Localised Prostate Cancer—A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
External factors, such as the coronavirus disease 2019 (COVID-19), can lead to cancellations and backlogs of cancer surgeries. The effects of these delays are unclear. This study summarised the evidence surrounding expectant management, delay radical prostatectomy (RP), and neoadjuvant hormone therapy (NHT) compared to immediate RP. MEDLINE and EMBASE was searched for randomised controlled trials (RCTs) and non-randomised controlled studies pertaining to the review question. Risks of biases (RoB) were evaluated using the RoB 2.0 tool and the Newcastle-Ottawa Scale. A total of 57 studies were included. Meta-analysis of four RCTs found overall survival and cancer-specific survival were significantly worsened amongst intermediate-risk patients undergoing active monitoring, observation, or watchful waiting but not in low- and high-risk patients. Evidence from 33 observational studies comparing delayed RP and immediate RP is contradictory. However, conservative estimates of delays over 5 months, 4 months, and 30 days for low-risk, intermediate-risk, and high-risk patients, respectively, have been associated with significantly worse pathological and oncological outcomes in individual studies. In 11 RCTs, a 3-month course of NHT has been shown to improve pathological outcomes in most patients, but its effect on oncological outcomes is apparently limited.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".