Impact of Social Determinants of Health on Post-operative Outcomes Following Robotic Radical Prostatectomy
Bibliographic record
Abstract
PURPOSE: Social determinants of health are increasingly recognized as key contributors to disparities in healthcare access and outcomes. With robotic-assisted radical prostatectomy now widely adopted as the preferred surgical approach for localized prostate cancer, this systematic review evaluates how individual social determinants of health influence access to robotic surgery and postoperative outcomes. MATERIALS AND METHODS: This review adhered to PRISMA guidelines and was registered with PROSPERO (CRD420256270179). A comprehensive search of PubMed and EBSCO identified studies examining social determinants of health in patients undergoing robotic prostatectomy. Extracted data included patient demographics, social determinants of health variables, and perioperative outcomes. Risk of bias was assessed using the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale. RESULTS: Eighteen studies met inclusion criteria. Commonly assessed variable included socioeconomic status, race/ethnicity, insurance, education, occupation, and geographic location. Lower socioeconomic status was linked to decreased robotic prostatectomy access, treatment at low-volume or non-robotic centers, and worse outcomes. Racial and ethnic disparities were consistent; non-White patients were less likely to receive definitive therapy and more likely to undergo surgery by low-volume providers. Rural patients experienced reduced access to robotic surgery and lower rates of pelvic lymph node dissection. Lower education levels were associated with delayed continence and reduced return-to-work capacity. CONCLUSIONS: Social determinants of health significantly impact access to robotic prostatectomy and postoperative outcomes. Urologists and policymakers should integrate awareness of these factors into patient counseling and institutional planning. Future research should explore mechanisms underlying these disparities to inform equity-driven strategies in prostate cancer care.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".