Internet Health Information–Seeking Trend of Urinary Incontinence in Mainland China: Infodemiology Study
Bibliographic record
Abstract
Background: Urinary incontinence (UI) is a series of clinical episodes featuring involuntary urine leakage. UI affects people in terms of their physical, emotional, and cognitive functioning, and the negative perceptions and impact on patients are not fully understood. In addition, the true demand for the treatment of UI and related issues is yet to be revealed. Objective: The aim of this study is to examine the online search trend, user demand, and encyclopedia content quality related to UI on a national and regional scale on Baidu search, the major search engine in Mainland China. Methods: The Baidu Index was queried using UI-related terms for the period from January 2011 to August 2023. The search volume for each term was recorded to analyze the search trend and demographic distributions. For user interest, the demand graph data and trend data were collected and analyzed. Results: Three search topics were identified with the 18 available UI search keywords. The total Baidu search index for all UI topics was 11,472,745. The annual percent changes (APCs) for the topic Complaint were 1.7% (P<.05) from 2011-2021 and -7.9% (P<.05) from 2021-2023, and the average annual percent change (AAPC) was 0.1% (P<.05). For the topic Inquiry, the APCs were 16% (P<.05) from 2011 to 2016, -27.00% from 2016 to 2019, and 21.2% (P<.05) from 2019 to 2023, with an AAPC of 4.8%. Regarding the topic of Treatment, the APC was 20.3% from 2011-2018 (P<.05), -36.9% from 2018-2021 (P>.05), and 2.2% from 2021-2023, with a -0.4% overall AAPC. The age distribution of the population of each UI search topic inquiry shows that the search inquiries for each topic were mainly made by the population aged 30 to 39 years. People from the eastern part of China made up around 30% of each search query. Conclusions: Web-based searching for UI topics has been continuous and traceable since January 2011. Different categorized themes within the UI topic highlight specific demands from various populations, necessitating tailored responses. Although online platforms can offer answers, medical professionals' involvement is crucial to avoid misdiagnosis and delayed treatment.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".