Does misinformation impact the perception of patients undergoing sexual health and other urological procedures: A cross sectional study
Bibliographic record
Abstract
Abstract Misinformation, particularly in sexual medicine and urology, is a rising concern for providers and patients alike. We aimed to assess where patients acquire information prior to their urologic consultation/procedure and assess patients’ perception as to the reliability of this information. A cross-sectional study at an outpatient men's health clinic included 314 consenting adult patients who independently completed the questionnaire (mean age: 51.2 ± 17.2). Overall, 55.1% of patients indicated they searched up their condition online. However, 39.2% and 27.7% of respondents agreed and strongly agreed respectively to misinformation being a big concern when searching for health information, p < 0.05. Only 59.9% of patients discussed with friends and those that did not, chose not wanting to (65.1%) as their top choice. However, 27.4% of respondents were embarrassed to do so. Similarly, 38.9% of respondents were embarrassed to do so. Finally, 38.2% and 12.4% of patients agreed and strongly agreed, that learning information prior to your doctor’s appointment affects their relationship with the physician, p < 0.05. These findings emphasize the need for urologists and sexual medicine specialists to be aware of where their patients are gathering health information and to address their concerns about misinformation.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".