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Record W2986989885 · doi:10.1186/s40463-019-0382-3

Idiopathic facial lipoatrophy in a healthy middle-aged woman: A case report

2019· article· en· W2986989885 on OpenAlexaffabout
Chad Purcell, S. Mark Taylor

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicDermatological and COVID-19 studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLipoatrophyMedicineHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

BACKGROUND: Facial lipoatrophy is a rare condition described by the disappearance of facial subcutaneous fat. The etiology of lipoatrophy can be congenital, or acquired including traumatic, iatrogenic or idiopathic. Idiopathic facial lipoatrophy has only been previously identified in three case reports, among which, the patient demographics vary considerably. Two of these case reports have identified a role for autologous fat transfer as a means of treatment. This case differs from those in the literature in patient demographics and severity of the facial lipoatrophy. The aim of the current report is to present a rare case of idiopathic facial lipoatrophy, and to assess the use of autologous fat transfer as a treatment modality. CASE PRESENTATION: We present a case of a 40-year old woman from Nova Scotia, Canada who presented with asymptomatic idiopathic facial lipoatrophy. The patient was otherwise healthy, taking no medications with no trauma or surgery in the region affected. Investigations, including a full autoimmune workup, were unremarkable. The facial lipoatrophy was considerably disfiguring and was assessed as a Grade 4 on the facial lipoatrophy scale. The patient was treated over the course of 2 years with two autologous fat transfers. CONCLUSIONS: Achieving resolution of idiopathic lipoatrophy is important to patients because it can manifest in a disfiguring form and have negative effects on quality of life. The current study reports a treatment of idiopathic lipoatrophy that achieved results acceptable to the patient.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.037
GPT teacher head0.289
Teacher spread0.252 · 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 designObservational
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

Citations2
Published2019
Admission routes2
Has abstractyes

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