Abstract Number ‐ 31: No Mercy on Stroke Campaign: The Use of Pop Culture Icons to Raise Stroke Awareness
Bibliographic record
Abstract
Introduction About 24–46% of acute ischemic strokes are due to large vessel occlusions1, however only a small fraction2,3 of thrombectomy eligible patients undergo emergent clot retrieval. Despite advances in thrombolytics and endovascular interventions, many patients are not candidates for emergent therapies due to delay in patient presentation, prehospital delay, triage delay and limited access to care. No Mercy on Stroke (NMOS) campaign aims to raise public stroke awareness by increasing social media footprint. Thereby enabling the general population with tools for earlier symptom detection, seeking rapid medical attention and fighting for legislature to improve prehospital networks. Methods Society of Vascular and Interventional Neurology (SVIN) launched NMOS campaign just prior to World Stroke Day (WSD) on October 25th, 2021 via Twitter. Pop culture icon and martialist Martin Kove launched the campaign as Sensei Kreese from the movie Cobra Kai in a video encouraging viewers to “strike fast and strike hard” when treating stroke. SVIN and Kove urged followers to spread the fight against stroke by sharing karate poses with hashtag #NoMercyOnStroke. Metrics such as social media reach, impact, location and others were extracted via Tweepsmap and Tweetbinder from October 25th, 2021 to August 20th, 2022. Results #NoMercyOnStroke was tweeted 716 times by 211 contributors4 across 19 countries and 77 cities5 for a potential reach of 374,200 people and potential impact of 2,051,908 people4. Tweet breakdown consisted of 126 original tweets and 590 retweets4. Engagements were primarily likes recorded at 2437, and followed by 97 replies and 55 quotes. Activity timeline was highest during week of WSD and accounted for majority exposure, however, there was another small peak in activity one month later. Top 3 countries involved were USA, Canada and India although USA accounted for 90.4% of all activity. Other countries include Mexico, Colombia, Spain, Egypt, Croatia, UK, Saudi Arabia, Kenya, Libya, Vietnam, Italy, Cuba, France, Peru, Chile and Venezuela5. Top associated hash tags were #worldstrokeday, #WSD and #alz0212465. Conclusions Use of pop culture icon as an advocate for stroke awareness greatly increased reach and impression of the NMOS campaign by touching nearly two million followers. Compared to raw data across three social media platforms for a similar campaign by Mission Thrombectomy called #BEFASTChallenge, #NoMercyOnStroke had exponentially more engagement with almost double the original posts and triple the retweets. Still, activity was concentrated around initial launch with minimal tail in the activity timeline. The overall impact of such campaigns on the end‐goal of decreasing the ratio of eligible thrombectomies to number of thrombectomies performed is yet to be uncovered. However, it is clear that larger campaigns involving celebrity influencers and community outreach are imperative to keeping the momentum of raising stroke awareness.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".