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Record W4401135081 · doi:10.12998/wjcc.v12.i27.6129

Insights into upper blepharoplasty: Conservative volume-preserving techniques

2024· article· en· W4401135081 on OpenAlexaff
Andrew Gorgy, R Hashemi, Johnny Ionut Efanov

Bibliographic record

VenueWorld Journal of Clinical Cases · 2024
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill University
Fundersnot available
KeywordsMedicineBlepharoplastyRejuvenationEyelidDemographicsEvidence-based medicineConservative managementSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

This editorial commentary critically examines the systematic review by Miotti et al , which discusses the evolving trends in upper lid blepharoplasty towards a conservative, volume-preserving approach. The review emphasizes the shift from traditional tissue resection to techniques that maintain anatomical integrity, paralleling broader trends in panfacial rejuvenation. Miotti et al delve into the nuances of fat pad management, advocating for conservation over reduction to sustain natural contours and improve long-term aesthetic outcomes. This perspective is supported by comparative studies and empirical data, such as those from Massry and Alghoul et al , highlighting the benefits of conservative approaches in terms of patient satisfaction and aesthetic longevity. The review also stresses the importance of surgeon discretion in adapting procedures to diverse patient demographics, particularly in addressing distinct features such as the Asian upper eyelid. However, it identifies a significant gap in long-term comparative research, underscoring the need for future studies to substantiate the safety and efficacy of these minimalist techniques. Overall, Miotti et al .'s work contributes profoundly to the discourse on personalized, conservative cosmetic surgery, urging ongoing research to refine and validate surgical best practices in upper eyelid blepharoplasty.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.427
Teacher spread0.352 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

Explore more

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