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Record W2753860325 · doi:10.1097/psn.0000000000000193

Hyaluronidase: Understanding Its Properties and Clinical Application for Cosmetic Injection Adverse Events

2017· article· en· W2753860325 on OpenAlexaff
Jeanine Harrison, Oriol Rhodes

Bibliographic record

VenuePlastic Surgical Nursing · 2017
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsThornhill Medical (Canada)
Fundersnot available
KeywordsAdverse effectHyaluronidaseMedicineHyaluronic acidSurgeryIntensive care medicinePharmacologyAnatomyBiology

Abstract

fetched live from OpenAlex

The recent global consensus on the management of cosmetic aesthetic injectable complications from hyaluronic acid (HA) has increased the focus on the use of hyaluronidase more than ever before (M. Signorini et al., 2016). A comprehensive knowledge of facial anatomy, including structural positioning of facial arteries and veins, and an extensive knowledge of HA products available for injection procedures, combined with best practice protocols, will assist to prevent adverse events. Despite the growing number of patients using cosmetic fillers for facial restoration, the incidents incidence of adverse events remains low. Indeed, the avoidance of complications through safe and effective injection practice remains the key to preventing the need to use hyaluronidase.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.153
GPT teacher head0.392
Teacher spread0.239 · 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 designOther design
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
Published2017
Admission routes1
Has abstractyes

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