Absence of Race/Ethnicity Reporting in Clinical Trials of True Minimally Invasive Surgical Therapies for the Treatment of Benign Prostatic Hyperplasia
Bibliographic record
Abstract
OBJECTIVE: To assess the extent of racial reporting and enrollment in randomized controlled trials (RCTs) of minimally invasive surgical therapies (MIST) for the office-based treatment of benign prostatic hyperplasia (BPH). METHODS: A systematic review was conducted for RCTs assessing 6 office-based MISTs: transurethral microwave thermotherapy (TUMT), prostatic artery embolization, prostatic urethral lift, temporary implantable nitinol device, water vapor thermal therapy, and Optilume. MEDLINE, Embase, and the Cochrane CENTRAL databases were searched up to November 3, 2023. Publications were excluded if they (1) did not address one of the aforementioned office-based MISTs for the treatment of BPH; (2) were not RCTs; (3) were an abstract or conference proceeding; or (4) were not published in English. In addition to study characteristics, data about racial reporting were collected. Two independent reviewers completed screening at title, abstract, and full-text levels, with conflicts resolved by consensus with a third reviewer. RESULTS: A total of 61 publications representing 37 unique RCTs (n = 4027 unique patients) were reviewed, with publication years spanning from 1993 to 2023. TUMT was the most frequently studied MIST. Most publications (79%) were based solely in Europe or North America. Over 50% of the publications were multicenter trials. None of the included publications reported on race/ethnicity of study participants. CONCLUSION: None of the 61 included publications of RCTs of office-based MISTs provided information on racial/ethnic composition of study participants. There is a staggering gap in the standardization of race/ethnicity reporting and enrollment within RCTs of MISTs. More granular data on race/ethnicity allow for better generalizability and equity.
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 | 0.299 | 0.639 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".