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Record W4416122201 · doi:10.1097/gox.0000000000007261

Tear Trough Rejuvenation With Injectable Biostimulatory Polynucleotides (VAMP)

2025· article· en· W4416122201 on OpenAlexaff
Noury Adel, Amira Gindi, Nenad Stankovic, Jesper Thulesen, Jack Kolenda, Ida Vega Thulesen

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2025
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTrough (economics)Adverse effectRejuvenationClinical PracticeAfrican descent

Abstract

fetched live from OpenAlex

Tear trough rejuvenation presents a clinical challenge due to thin skin, superficial vasculature, and age-related volume loss. VAMP, manufactured by Prollenium, a polynucleotide-based biostimulatory injectable, is designed to support dermal regeneration and improve skin quality without volumizing excess. The objective of this study was to evaluate early clinical outcomes after VAMP injection in the tear trough area. Five healthy female patients (aged 35-50 y) of Middle Eastern and White descent were treated with 2 mL of VAMP (1 mL per side) using a 23G 30-mm SoftFil microcannula. The product was delivered via a combined microdroplet and linear threading technique to enhance distribution. Follow-up assessments were conducted at 1 week, 2 weeks, 1 month, and 3 months. Evaluation included standardized clinical photography, patient-reported satisfaction, and assessments of texture, tone, pigmentation, and contour. Mild to moderate improvements were observed in tear trough appearance, skin smoothness, and pigmentation. No adverse events were reported within the follow-up period. However, the limited sample size and short duration preclude definitive conclusions. Preliminary results suggested that VAMP may offer a promising option for tear trough rejuvenation. Further studies with larger cohorts and extended follow-up beyond 6 months are warranted.

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)0.001
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 score0.048
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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