#Fake news: a systematic review of mechanical thrombectomy results among neurointerventional stroke surgeons on Twitter
Bibliographic record
Abstract
OBJECTIVE: Twitter is a popular social media platform among physicians. Neurointerventionalists frequently document their lifesaving mechanical thrombectomy cases on Twitter with very favorable results. We fear that there may be some social media publication bias to tweeted mechanical thrombectomy cases with neurointerventionalists being more likely to tweet cases with favorable outcomes. We used these publicly documented cases to analyze post-intervention Twitter-reported outcomes and compared these outcomes with the data provided in the gold standard literature. METHODS: Two reviewers performed a search of Twitter for tweeted cases of acute ischemic strokes treated with mechanical thrombectomy. Data were abstracted from each tweet regarding baseline characteristics and outcomes. Twitter-reported outcomes were compared with the Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke (HERMES) trial individual patient meta-analysis. RESULTS: When comparing the tweeted results to HERMES, tweeted cases had a higher post-intervention rate of modified Thrombolysis In Cerebral Infarction (mTICI) scale score of 2c/3 (94% vs 71%, respectively; p<0.0001) and rate of National Institutes of Health Stroke Scale (NIHSS) score ≤2 (81% vs 21%, respectively; p<0.0001). There were no reported complications; thus, tweeted cases also had significantly lower rates of complications, including symptomatic intracerebral hemorrhage (0% vs 4.4%, respectively; p<0.0001), type 2 parenchymal hemorrhage (0% vs 5.1%, respectively; p<0.0001), and mortality (0% vs 15.3%, respectively; p<0.0001). CONCLUSIONS: There is a significant difference between social media and reality even within the 'MedTwitter' sphere, which is likely due to a strong publication bias in Twitter-reported cases. Content on 'MedTwitter', as with most social media, should be accepted cautiously.
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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 | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".