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Record W4312066532 · doi:10.1097/gox.0000000000004639

Ultrasound as an Educational Tool in Facial Aesthetic Injections

2022· article· en· W4312066532 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2022
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsCanadian Institute of Mining, Metallurgy and Petroleum
Fundersnot available
KeywordsUltrasoundVisualizationInjectorMedicineSAFERBiomedical engineeringMedical physicsComputer scienceRadiologyArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Injection therapies for cosmetic enhancement, particularly antiaging treatments, are increasingly popular. However, once the needle has penetrated the skin, the injector is "blind" to the depth and exact location of the needle tip. Duplex ultrasound use before and after treatment can allow the injector to visualize in real time the individual anatomy, thereby improving and confirming the accuracy of the injections through visualization of both the target layer and the vital structures to be avoided. Previously injected permanent filler treatments can also be visualized. In this way, ultrasound use becomes an important educational tool in promoting "safer" facial injection therapy. It shifts static anatomy to mobile real-time facial anatomy, thereby establishing itself as an invaluable learning tool through follow-up imaging, with subsequent optimization in techniques and patient outcomes.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.024
GPT teacher head0.316
Teacher spread0.292 · 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