Recommendations for cutaneous and aesthetic surgeries during COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Amid the coronavirus disease 2019 (COVID-19) pandemic, dermatologists must be prepared to restructure their practice of procedural dermatology and cutaneous aesthetic surgeries. The COVID-19 pandemic has presented several challenges and has ushered in several changes in practice such as teledermatology, with many physicians adopting virtual consultations and treatments. Performing procedures in the times of COVID-19 pandemic presents challenges such as risk of transmission to doctors and staff due to potential aerosolization, release of virus droplets during the procedures, and risk of virus transfer through the instruments both in the peri- and postoperative period. This can have several medical, administrative, and legal implications. OBJECTIVES: This document aimed to outline best practices that can be followed in this scenario to perform cutaneous surgeries and procedures to ensure safer skin surgery. RECOMMENDATIONS: Standard precautions include social distancing of at least 1 m, hand hygiene, appropriate use of personal protective equipment (PPE), safe injection practices, sterilization and disinfection of medical devices, environmental cleaning, and respiratory hygiene. It is generally advisable to see patients only by appointments. Each clinic should have a special area at entry for screening patients and providing sanitizers and masks. Procedures, which are of short duration, performed on nonfacial areas are considered as low risk and require donning surgical mask. Procedures involved with minimal invasiveness and bleeding, short duration procedures on the face such as injectables, chemical peels, and aerosol-generating procedures on nonfacial areas are considered moderate risk. These procedures need apron with head cover, N95 mask, face shield, double gloves, and smoke evacuator with high-efficiency particulate air (HEPA) or ultralow particulate air (ULPA) filter. Aerosol and plume-regenerating procedures (such as ablative lasers on the face), prolonged surgeries on head (such as hair transplantation), intraoral, and intranasal procedures are considered high risk. These procedures must be carried out with full body cover with surgical gown, head cover, N95 mask, face shield, double gloves, and smoke evacuator. Physicians should be aware of local epidemiological situation and adhere to the relevant guidelines issued by the relevant governmental agencies.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.014 |
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".