Impact of Coronavirus disease 2019 on patients with toothache
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to investigate the impact of Coronavirus disease 2019 (COVID-19) on toothache patients through posts on Sina Weibo. METHODS: Using Gooseeker, we searched and screened 24,108 posts about toothache on Weibo during the dental clinical closure period of China (February 1, 2020-February 29, 2020), and then divided them into 4 categories (causes of toothache, treatments of toothache, impacts of COVID-19 on toothache treatment, popular science articles of toothache), including 10 subcategories, to analyze the proportion of posts in each category. RESULTS: There were 12,603 postings closely related to toothache. Among them, 87.6% of posts did not indicate a specific cause of pain, and 92.8% of posts did not clearly indicate a specific method of treatment. There were 38.9% of the posts that clearly showed that their dental treatment of toothache was affected by COVID-19, including 10.5% of the posts in which patients were afraid to see the dentists because of COVID-19, and 28.4% of the posts in which patients were unable to see the dentists because the dental clinic was closed. Only 3.5% of all posts were about popular science of toothache. CONCLUSIONS: We have studied and analyzed social media data about toothache during the COVID-19 epidemic, so as to provide some insights for government organizations, the media and dentists to better guide the public to pay attention to oral health through social media. Research on social media data can help formulate public health policies.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".