Public Interest in Online Information on Recurrent Urinary Tract Infections Is Greatest for Information with the Poorest Publication Quality
Bibliographic record
Abstract
Background: Urinary tract infections (UTIs) are among the most prevalent bacterial infections. With many patients turning to the Internet as a health resource, this study seeks to understand public engagement with online resources concerning recurrent UTIs (rUTIs), assess their reliability, and identify common questions/concerns about rUTIs. Methods: Social media analysis tool BuzzSumo was used to calculate online engagement (likes, shares, comments, views) with information on rUTIs. The reliability of highly engaged articles was evaluated using the DISCERN questionnaire. Highly engaged categories were entered as keywords in Google Trends to quantify search interest. To categorize patient-specific concerns, a database containing anonymously collected patient questions about rUTIs was created. Results: BuzzSumo revealed four search categories: general information, treatment, causes, and herbal remedies. DISCERN scores indicated moderate reliability overall; however, the “herbal remedies” category demonstrated poor reliability despite high engagement. Google Trends analysis highlighted “causes” and “treatment” searches as highest in relative interest. The 10 most popular categories of concern were antibiotics, microbiome, vaccines, prevention, pelvic pain, sex, testing, symptoms, diet/lifestyle, and hormones. Conclusions: People living with rUTIs demonstrate key concerns and often seek information online, yet articles with high engagement often contain unreliable information. Healthcare professionals may consider counteracting misinformation by providing evidence-based information online about rUTIs.
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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 | 0.040 | 0.287 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".