#Cleftlip/Palate: What Is the World Talking About?
Bibliographic record
Abstract
OBJECTIVE: Studies have begun analyzing how the world converses on social media platforms about medical/surgical topics. This study's objective was to examine how cleft lip and palate, two of the most common birth defects in the world, are discussed on the social media platform Twitter. No study to date has analyzed this topic. METHODS: Tweets were identified using any of the following: cleft, cleft lip, cleft palate, #cleft, #cleftlip, #cleftpalate. Eight months between 2017 and 2018 were analyzed. MAIN OUTCOME MEASURES: The primary outcome was the tweet subject matter. Secondary outcomes were author characteristics, tweet engagement, multimedia, and tweet accuracy. RESULTS: = .03). Twenty-seven countries tweeted, with the United States (34%) and India (27%) producing the most. Charities (36%), hospitals (14%), and physicians (13%) were the most common authors. Over three-quarters of tweets were self-promotional. The top content included charity information (22%) and patients' cleft stories (14%). Tweets about patient safety/care and surgical service trips generated the most engagement. The accuracy of educational tweets was 38% low accuracy and 1% inaccurate. One hundred forty-nine tweets (12%) discussed a published research article, but 41 tweets did not share a link. CONCLUSIONS: Charities dominate the cleft lip/palate "Twitterverse." Most tweets were self-promotional, and over a third of educational tweets were low accuracy. As the cleft social media community continues to grow, we recommend using the hashtag #cleft to reach a wider audience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".