MétaCan
Menu
Back to cohort
Record W4284690641 · doi:10.1136/bcr-2022-249190

Hyperbaric oxygen therapy for treatment of a late presenting ischaemic complication from hyaluronic acid cosmetic filler injection

2022· article· en· W4284690641 on OpenAlexafffund
Farhang Jalilian, Samuel P Hetz, J Bostwick, Sylvain Boet

Bibliographic record

VenueBMJ Case Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsInstitut du Savoir MontfortOttawa HospitalMontfort HospitalUniversity of Ottawa
FundersOttawa Hospital Anesthesia Alternate Funds AssociationUniversity of Ottawa
KeywordsHyaluronic acidHyaluronidaseMedicineComplicationHyperbaric oxygenSurgeryAnesthesia

Abstract

fetched live from OpenAlex

Vascular compromise and resulting ischaemic injury are known rare complications of cosmetic filler injections. Most hyaluronic acid vascular compromises present early and can be treated effectively by hyaluronidase. Here we present a case of ischaemic wound and mucosal necrosis after cosmetic facial hyaluronic acid injection that appeared within hours of injection but was not diagnosed and treated for 5 days. At day 5, the patient was treated with hyaluronidase injection immediately followed by 14 sessions of daily hyperbaric oxygen therapy (HBOT). Despite the delayed treatment, the patient had essentially complete recovery and the hyperbaric therapy was overall well-tolerated. Our case report suggests that hyaluronidase injection with concurrent daily HBOT sessions may be effective to allow recovery from late-presenting filler ischaemic complication. Furthermore, given the safety profile of HBOT, we suggest a more deliberate approach to this modality as a therapeutic adjunct by cosmetic practitioners when similar complications arise.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.344
Teacher spread0.290 · 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 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

Citations12
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueBMJ Case ReportsSame topicFacial Rejuvenation and Surgery TechniquesFrench-language works237,207