A Self-Selection Validation Study of the Uresta Bladder Support
Bibliographic record
Abstract
OBJECTIVES: When a medical device is available over the counter, the ability of a consumer to correctly self-select to use the device, independent of guidance from a health care professional, is essential and important. This study was undertaken to evaluate the self-selection process for the Uresta Bladder Support. METHODS: A total of 49 women were enrolled in this study. The results of a self-selection interview were validated by using a gynaecologic examination as the gold standard. RESULTS: The urinary continence diagnoses broke down as follows: continent 16 (33%), pure stress incontinence 18 (37%), mixed urinary incontinence 13 (27%), and pure urge incontinence 2 (4%). A total of 36 (73%) indicated that they would acquire the bladder support and use it, whereas 13 (27%) indicated that they would not choose to use the device based on their understanding of the device and their personal medical history. A total of 43 (88%) made a correct self-selection decision and 6 (12%) made an incorrect decision. Root cause analysis found that the residual risks associated with use of the Uresta Bladder Support in the over-the-counter context were acceptable and outweighed by the impact of the device on user's quality of life. CONCLUSIONS: Using the information provided on the external packaging of the Uresta Bladder Support, most users will make a correct self-selection decision regarding the use of the product to manage their incontinence symptoms.
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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".