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Record W2541236113 · doi:10.1097/dss.0000000000000898

Prevention and Management of Injection-Related Adverse Effects in Facial Aesthetics: Considerations for ATX-101 (Deoxycholic Acid Injection) Treatment

2016· review· en· W2541236113 on OpenAlexaboutno aff
Steven Fagien, Patricia A. McChesney, Meenakshi Subramanian, Derek Jones

Bibliographic record

VenueDermatologic Surgery · 2016
Typereview
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLidocaineAdverse effectAnesthesiaClinical trialPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

ATX-101 (deoxycholic acid injection; Kythera Biopharmaceuticals, Inc. [an affiliate of Allergan plc, Dublin, Ireland]) was approved in 2015 in the United States (Kybella) and Canada (Belkyra) for submental fat reduction. As expected, injection-site reactions such as pain, swelling, and bruising, which were mostly mild or moderate and transient, were common adverse events (AEs) reported in clinical trials. An exploratory Phase 3b study investigating interventions for management of injection-site AEs associated with ATX-101 treatment was recently completed. Based on its results, literature review, and our clinical experiences, we have put forward considerations for management of AEs associated with ATX-101 treatment in clinical practice. Pretreatment with oral ibuprofen and/or acetaminophen an hour before treatment and preinjection with epinephrine-containing buffered lidocaine 15 minutes before treatment can help with management of pain and bruising. Cold application to the treated area before and immediately after the procedure may help to reduce pain (if local anesthetic preinjection is not performed) and swelling. Discontinuing medications/supplements that result in increased anticoagulant or antiplatelet activity 7 to 10 days before ATX-101 treatment, when possible, can reduce the risk of bruising. In summary, injection-site AEs associated with ATX-101 treatment can be effectively managed with commonly used interventions.

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)0.003
metaresearch head score (Gemma)0.005
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.053
GPT teacher head0.327
Teacher spread0.273 · 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
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

Citations40
Published2016
Admission routes1
Has abstractyes

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