Active surveillance in favorable intermediate-risk prostate cancer patients: Predictors of deferred intervention and treatment choice
Bibliographic record
Abstract
INTRODUCTION: Active surveillance (AS) is increasingly used for favorable intermediate-risk (FIR) prostate cancer (PCa). Our objective was to determine oncological and sociodemographic predictors of deferred definitive therapy and decision for radical prostatectomy (RP) vs. radiotherapy (RT). METHODS: The Surveillance, Epidemiology, and End Results (SEER) Prostate with Watchful Waiting database was used to identify all FIR PCa diagnosed between 2010 and 2015 opting for AS for at least one year following diagnosis. We sought to determine predictors of treatment and treatment type using multivariable logistic regression. RESULTS: A total of 20 334 patients were identified. An annual decrease in incident FIR patients managed initially with AS between 2010 (4061) and 2015 (2947) was noted (p for trend <0.001); 17 895 (88.0%) patients underwent deferred RP and/or RT. Patients with higher baseline cancer volume and clinical stage were significantly more likely to discontinue AS. Patients of higher socioeconomic status were more likely to undergo deferred therapy, with increased odds for RT over RP. African American patients had lower odds of undergoing definitive intervention (odds ratio 0.83, p=0.030) and were significantly more likely to opt for XRT. Oncological characteristics leading to FIR classification influenced treatment choice at the time of deferred intervention: RT was treatment of choice in 86.3% and 86.0% of Gleason group 2 and prostate-specific antigen 10-20 FIR patients, respectively; 96.1% of treated cT2b-c FIR patients opted for RP. CONCLUSIONS: Most FIR PCa patients initially managed with AS eventually undergo deferred definitive therapy, with choice of treatment significantly influenced by patients' baseline oncological and sociodemographic characteristics.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".