MétaCan
Menu
Back to cohort
Record W2900883020 · doi:10.2147/ccid.s180904

A 10-point plan for avoiding hyaluronic acid dermal filler-related complications during facial aesthetic procedures and algorithms for management

2018· article· en· W2900883020 on OpenAlexaff
Izolda Heydenrych, Krishan Mohan Kapoor, Koen De Boulle, Greg Goodman, Andrew J. Swift, Narendra Kumar, Ebad Ur Rahman

Bibliographic record

VenueClinical Cosmetic and Investigational Dermatology · 2018
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsCanadian Institute of Mining, Metallurgy and Petroleum
FundersUniversiteit Stellenbosch
KeywordsContext (archaeology)MedicineProduct (mathematics)DocumentationFiller (materials)Plan (archaeology)Operations managementRisk analysis (engineering)SurgeryAlgorithmComputer scienceEngineering

Abstract

fetched live from OpenAlex

The recent rapid growth in dermal filler use, in conjunction with inadequate product and injector control, has heralded a concerning increase in filler complications. The 10-point plan has been developed to minimize complications through careful preconsideration of causative factors, categorized as patient, product, and procedure related. Patient-related factors include history, which involves a preprocedural consultation with careful elucidation of skin conditions, systemic disease, medications, and previous cosmetic procedures. Other exclusion criteria include autoimmune diseases and multiple allergies. The temporal proximity of dental or routine medical procedures is discouraged. Insightful patient assessment, with the consideration of ethnicity, gender, and generational needs, is of paramount importance. Specified informed consent is vital due to the concerning increase in vascular complications, which carry the risk for skin compromise and loss of vision. Informed consent should be signed for both adverse events and their treatment. Product-related factors include reversibility, which is a powerful advantage when using hyaluronic acid (HA) products. Complications from nonreversible or minimally degradable products, especially when layered over vital structures, are more difficult to control. Product characteristics such as HA concentration and proprietary cross-linking should be understood in the context of ideal depth, placement, and expected duration. Product layering over late or minimally degradable fillers is discouraged, while layering of HA of over the same brand, or even across brands, seems to be feasible. Procedural factors such as photographic documentation, procedural planning, aseptic technique, and anatomical and technical knowledge are of pivotal importance. A final section is dedicated to algorithms and protocols for the management and treatment of complications such as hypersensitivity, vascular events, infection, and late-onset nodules. The 10-point plan is a systematic, effective strategy aimed at reducing the risk of dermal filler complications.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0210.007

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 head0.074
GPT teacher head0.362
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations120
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueClinical Cosmetic and Investigational DermatologySame topicFacial Rejuvenation and Surgery TechniquesFrench-language works237,207