Teste Talk: A trial social media campaign to improve awareness of testicular torsion
Bibliographic record
Abstract
INTRODUCTION: Adolescent males are particularly prone to testicular torsion, often resulting in subsequent orchiectomy. A lack of knowledge about testicular pathologies, as well as hesitancy to discuss genital concerns, are fundamental, preventable barriers to early presentation. We hypothesized that a social media campaign to improve awareness of testicular torsion and other urological conditions affecting adolescents may overcome such barriers in this population. METHODS: A social media campaign, "Teste Talk," was created and promoted on Instagram and Facebook. Data was collected from June 1 to December 1, 2021. Instagram followers, Facebook page likes, Instagram and Facebook reach, post likes, Instagram follower demographics, and advertisement data were reviewed. Data was collected using Meta Business Suite. Paid promotions to improve awareness of the campaign were targeted towards 13-18-year-old males in Alberta and were funded by the Undergraduate Research Initiative Support Fund. RESULTS: The campaign reached 26 072 Instagram accounts and 14 741 Facebook accounts. The Instagram page amassed 382 followers, while the Facebook page accumulated 99 likes. Paid advertisements were seen 81 136 times on Instagram and Facebook. Instagram surveys demonstrated that over the study period, followers had an increased awareness of testicular torsion and how to recognize it. No patients presenting with torsion during the study period admitted to seeing the campaign. CONCLUSIONS: Testicular torsion remains a significant issue among adolescent males, and creative ways to disseminate information and increase knowledge and conversations about testicular pathologies are needed. Social media campaigns present a potential pathway for increasing awareness and reducing delays to presentation and orchiectomies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".