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Record W4417523938 · doi:10.1097/dss.0000000000004970

Does Prior Treatment With Facial Injectables Increase the Risk of Rhytidectomy Complications?

2025· article· en· W4417523938 on OpenAlexaff
Jennifer Salsberg, Dimitrios Motakis

Bibliographic record

VenueDermatologic Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRhytidectomyFace surgeryFacial rejuvenationWrinkleMEDLINERhytidoplasty

Abstract

fetched live from OpenAlex

BACKGROUND: It is unknown whether facial injectable treatments before rhytidectomy increase the risk of surgical complications, and data on the safety of rhytidectomy after prior injectable (PI) treatments are essential for clinicians and patients. OBJECTIVE: The authors sought to determine whether facial injectable treatments increase complications from rhytidectomy surgery. MATERIALS AND METHODS: The authors performed a retrospective chart review of all patients who had a deep-plane rhytidectomy over a 15-month period. The authors recorded PIs comprising HA, calcium hydroxylapatite, and/or poly- l -lactic acid, or no prior injectables (NPI) to determine whether patients with prior facial injectables were at increased risk of complications after rhytidectomy. RESULTS: More than half of patients (57%) had received injectable treatments before surgery. Twenty five patients (24%) had complication after rhytidectomy. Of these, 17 had PI and 8 had NPI. The rate of complications among all PI patients was 17/106 (16%), and the rate of complications among all NPI patients was 8/106 (8%), which is not statistically significant p = .188. CONCLUSION: Their study demonstrates no significant increased risk of complications after rhytidectomy in patients with a history of facial injectable procedures. To the best of the authors' knowledge, this is the first study documenting the safety of rhytidectomy after PI procedures.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.350
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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