Aerosol-Generating Procedures and Simulated Cough in Dental Anesthesia
Bibliographic record
Abstract
Dental professionals are at an increased risk for exposure to the severe acute respiratory syndrome coronavirus 2 with aerosol-generating procedures (AGPs), and dental anesthesia practices have additional risks due to airway management procedures. The purpose of this pilot study was to examine the extent of splatter on dental personnel that may occur with AGPs and coughing in a dental anesthesia practice. A Dentoform model was fitted into a dental mannequin and coated with Glo Germ to detect splatter during simulated dental AGPs produced with use of a high-speed handpiece, an ultrasonic scaler, and an air-water syringe, all in conjunction with high-volume suction. A simulated cough was also created using a ventilator programmed to expel Glo Germ within the velocity and volume parameters of a natural cough with dental personnel in their customary positions. A UV light was used after each procedure to systematically evaluate the deposition of Glo Germ splatter on each person. After AGPs were performed, splatter was noted on the face, body, arms, and legs of the dentist and dental assistant. The simulated cough produced more extensive splatter than AGPs; additional Glo Germ was seen on the shoes, the crown of the head, and the back of the dental personnel. Therefore, it is recommended that full personal protective equipment consistent with AGPs be used and changed between patients to reduce the risk of contamination and infection for dental personnel and patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".