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The use of polymethyl-methacrylate (Artecoll) as an adjunct to facial reconstruction

2004· article· en· W4243873891 on OpenAlex
David W. S. Mok

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 Surgery · 2004
Typearticle
Languageen
FieldMedicine
TopicReconstructive Facial Surgery Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsPolymethyl methacrylateAdjunctMethacrylateMaterials scienceComposite materialPhilosophyPolymer

Abstract

fetched live from OpenAlex

BACKGROUND: Injectable polymethyl-methacrylate (PMMA) microspheres, or Artecoll, has been used for the last few years in aesthetic surgery as long-term tissue filler for the correction of wrinkles and for lip augmentation. This paper presents three cases of the use of PMMA microsphere injection for reconstructive patients with defects of varying etiologies. These cases provide examples of a novel adjunct to the repertoire of the reconstructive surgeon. OBJECTIVES: To evaluate the effectiveness (short-and long-term) of PMMA injection for the correction of small soft tissue defects of the face. METHODS: Three case histories are presented. They include the origin of the defect; previous reconstructions of the defect; and area, volume, timing and technical particularities of PMMA administration. RESULTS: All three cases showed improvement of the defect with the PMMA injection with respect to both objective evidence and patient satisfaction. The improvements can still be seen after several years. CONCLUSIONS: PMMA microsphere injection can be effectively used to correct selected small facial defects in reconstructive cases and the results are long lasting.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.048
GPT teacher head0.284
Teacher spread0.236 · 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