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Record W3191179479 · doi:10.46747/cfp.6708582

The COVID-19 pandemic and its skin effects

2021· review· en· W3191179479 on OpenAlexaffvenue
Anthony Zara, Patrick Fleming, Kyle Lee, Charles Lynde

Bibliographic record

VenueCanadian Family Physician · 2021
Typereview
Languageen
FieldMedicine
TopicDermatological and COVID-19 studies
Canadian institutionsLynde Centre for Dermatology
Fundersnot available
KeywordsMedicinePandemicPsychosocialContext (archaeology)Coronavirus disease 2019 (COVID-19)DermatologyPersonal protective equipmentDiseaseInfectious disease (medical specialty)PathologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To review the current literature on cutaneous diseases associated with the global coronavirus disease 2019 (COVID-19) pandemic, and to provide a general overview for family physicians of dermatologic presentations associated with COVID-19. QUALITY OF EVIDENCE: . Additional terms were personal protective equipment (PPE), hand hygiene, and psychosocial factors affecting skin diseases. Only English-language literature was reviewed. Evidence ranged from levels I to III. MAIN MESSAGE: Coronavirus disease 2019 is associated with a range of cutaneous presentations through direct infection with severe acute respiratory syndrome coronavirus 2, such as maculopapular, vesicular, pseudo-chilblain, livedoid, necrotic, urticarial, and Kawasaki-like rashes. Indirect presentations secondary to behavioural modifications are associated with use of personal protective equipment and sanitization procedures. Furthermore, psychosocial factors and stress associated with the pandemic also exacerbate pre-existing skin conditions. CONCLUSION: The COVID-19 pandemic has increased rates of dermatologic conditions through direct infection, behavioural changes, and association with psychosocial factors. As the incidence of COVID-19 increases, family physicians should be well equipped to diagnose and manage dermatologic presentations as they change within the context of the pandemic.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.086
GPT teacher head0.344
Teacher spread0.258 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2021
Admission routes2
Has abstractyes

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