Evidence map›Paper›PMID 39935795›Full record

ArticleAesthetic surgery journal. Open forum2025

Exploring Available Plastic Surgery Reward Programs and Proposing a Modeled Approach.

Logan G Galbraith, Daniel Najafali, Gregory A Greco, Raman Mehrzad

Abstract read
In one paragraph

Article in Aesthetic surgery journal. Open forum, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gregory A Greco
Raman Mehrzad

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is a paucity of literature exploring private practice reward programs within plastic and aesthetic surgery. In this study, the authors explore private practice websites and identify universal commercial programs and practice-specific programs being adopted. They aim to evaluate the current landscape of private practice reward programs by examining their advantages and limitations. Additionally, they propose an "ideal" loyalty program that could enhance patient engagement, satisfaction, and practice growth. A review was performed to identify available reward programs and models. Private practice websites were examined for available reward models. Commercial loyalty programs were cross-referenced to determine practices leveraging them. Data collected included private practice reward program type, region, and total surgeons at the practice. Advantages and areas of improvement were analyzed of existing programs and discussions with private practice surgeons generated proposals of an ideal loyalty program model. There were no articles on plastic surgery reward programs. Private practice websites suggested 3 universal reward systems, including Allē (Allergan, Inc., Dublin, Ireland), Aspire (Galderma, Lausanne, Switzerland), and Evolus (Newport Beach, CA). A total of 4 private practices with custom reward programs were identified. Universal reward systems are point-based and often lack excitement for the consumer. These benefits accumulate points across different providers, but often lack practice-specific reward opportunities. Although beneficial, most commercially available loyalty programs do not integrate into electronic medical records (EMRs) to track usage and challenges exist in tracking patient utilization. The ideal platform harmonizes the advantages of commercial loyalty programs with tailored practice-specific rewards while having the capacity to integrate with the EMR and capture patient utilization. Level of Evidence 5 Therapeutic:

Identifiers

PMID39935795
PMCPMC11811035

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.