Covid-19 outbreak and Oral Health Concerns – A Systematic Review
Bibliographic record
Abstract
Abstract Background:The aim of this systematic review is to shed light on practical implications of Covid-19 pandemic for the profession of dentistry. It examines the current literature and dental guidelines on Covid-19 in a systematic manner.Methods:A sequential systematic literature search was conducted on Pubmed, Medline, CINAHL, Scopus, Google scholar, Embase of Web of Science. The search results yielded the following results-Covid-19 (n=5171), Novel corona virus (n=63), Covid- 19 and dentistry (n=46), Covid-19 and oral health (n=41) Novel Corona virus and Dentistry (n=0), dental health and Novel Coronavirus (n=26), and dental practice and Novel Coronavirus (n=6)Results:The final review included 13 articles after elimination of other articles based on inclusion and exclusion criteria. Original articles and systematic reviews addressing 2019-nCoV and dentistry were entitled for inclusion, case reports, case series, correspondences, editorials were not included. Bias risk assessment was assessed using the Newcastle-Ottawa Scale (NOS)Conclusion:Covid-19 pandemic is an existential crisis for the profession of dentistry and requires a complete rethink about many aspects of the profession due to the nature of dental work. Evidence based research and multi-sectorial collaboration is required to make the profession safe again, both for the patient and dental team.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".