MétaCan
Menu
Back to cohort
Record W3205587543 · doi:10.1093/asj/sjab364

Aesthetic Surgery Practice Resumption in the United Kingdom During the COVID-19 Pandemic

2021· article· en· W3205587543 on OpenAlexaff
Nikita Joji, Nakul Patel, Nora Nugent, N Patel, Manish Mair, Shailesh Vadodaria, Norman Waterhouse, Venkat Ramakrishnan, Thangasamy K Sankar

Bibliographic record

VenueAesthetic Surgery Journal · 2021
Typearticle
Languageen
FieldPsychology
TopicBody Image and Dysmorphia Studies
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsMedicinePandemicCoronavirus disease 2019 (COVID-19)AuditElective surgerySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakSurgeryGeneral surgeryDiseaseInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.164
GPT teacher head0.381
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAesthetic Surgery JournalSame topicBody Image and Dysmorphia StudiesFrench-language works237,207