Bibliographic record
Abstract
BACKGROUND The social media site Twitter has been widely embraced in medical circles for its ability to connect individuals and support rapid information sharing. Critics say that the messages shared may not accurately reflect what was said and that sharing meeting content could devalue conferences themselves. It is unclear how it is used at SSIEM and what value it may bring. METHODS Twitter’s tweetdeck software was used to find all tweets containing the conference ‘hashtag’ #SSIEM2018. All tweets were reviewed to identify the author, see what had been shared and count replies, likes and retweets. Authors were grouped by professional background and tweet content was broken down by type of material shared and theme. RESULTS 122 relevant tweets were sent during the fortnight at the beginning of September 2018, creating over 400,000 impressions. There were a further 73 replies with approximately 13 engagements (likes, replies or retweets) per tweet. 36 people wrote tweets (rate: 3.4 per person [1-33]). One quarter of the tweets shared poster content and over one third of tweets related to Phenylketonuria materials. 50 of the tweets were produced by just two accounts, both intended to provide information to patients and their families. DISCUSSION Tweets where no hashtag was used cannot be identified and restrictions within Twitter prevent certain analyses on tweet data greater than 30 days old. However, Twitter uptake within metabolic medicine is significantly behind other specialities where conference tweets can exceed 20,000. Information shared is typically intended for patients rather than other health professionals; this suggests a different uptake to more mainstream specialities. Presenting teams should be aware that their work may be received directly by patients and families and consider how best to present their messages for all who may receive them.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".