Aesthetic Surgery Practice Resumption in the United Kingdom During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: The global COVID-19 pandemic has significantly impacted all aspects of healthcare, including the delivery of elective aesthetic surgery practice. A national, prospective data collection was carried out of the first aesthetic plastic surgery procedures performed during the COVID-19 pandemic in the United Kingdom. OBJECTIVES: The aim of this study was to explore the challenges aesthetic practice is facing and to identify if any problems or complications arose from carrying out aesthetic procedures during the COVID-19 pandemic. METHODS: Over a 6-week period from June 15 to August 2, 2020, data were collected by means of a proforma for aesthetic plastic surgery cases. All patients had outcomes recorded for an audit period of 14 days postsurgery. RESULTS: The results demonstrated that none of the 371 patients audited who underwent aesthetic surgical procedures developed any symptoms of COVID-19-related illness and none required treatment for any subsequent respiratory illness. CONCLUSIONS: No COVID-19-related cases or complications were found in a cohort of patients who underwent elective aesthetic procedures under strict screening and infection control protocols in the early resumption of elective service.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".