Deep brain stimulation in children and youth: perspectives of patients and caregivers gleaned through Twitter
Bibliographic record
Abstract
OBJECTIVE: This study aims to glean patient and caregiver perspectives surrounding deep brain stimulation (DBS) in children and youth through an analysis of patterns of social media usage. METHODS: The authors performed a comprehensive search of the Twitter Application Programming Interface (API) database for all tweets about DBS use in children and youth, with no date restriction. Data pertaining to each tweet were extracted for analysis. Results were analyzed using qualitative and quantitative methodologies. These included thematic analysis of tweets, accounts, and descriptive statistics. Sentiment analysis of extracted tweets was also performed. A multivariable regression model was used to identify predictors of higher engagement metrics (likes, retweets, and quotes). RESULTS: A comprehensive search of the Twitter database yielded 877 tweets from 816 unique accounts meeting study inclusion criteria. Most tweets were from patients or caregivers, researchers, or news media outlets. The most common themes among analyzed tweets were research discussing novel findings (45.2%) or personal experiences of patients or caregivers (27.4%). Sentiment analysis showed that 54.5% of tweets were positive, 35.1% were neutral, and 10.4% were negative. The presence of pictures or videos increased the tweet engagement count by an average of 10.5 (95% CI 7.3-13.6). Tweets about personal patient experiences (β = 6, 95% CI 0.95-12) and tweets tagging other accounts (β = 3.2, 95% CI 0.63-5.8) were also significantly associated with higher engagement metrics. CONCLUSIONS: The current study is the first to assess patient and caregiver perspectives surrounding pediatric DBS through a comprehensive analysis of social media usage. Given the nascent field, social media presents an opportunity to share experiences and promote patient and healthcare professional education surrounding pediatric DBS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".