Evidence map›Paper›PMID 42610032›Full record

ReviewCureus2026

Influence of Social Media on the Demand for Esthetic Dental Treatment: Patient Expectations, Risks, and Professional Responsibility.

Lixmer Rivas Pinto, Pierina Morales Oviedo, Yoley C Paredes Saavedra, Eleazar Garcia Alvarez

Abstract readReview
In one paragraph

Review in Cureus, 2026. 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.

Lixmer Rivas PintoDentistry, A.T. Still University, Kirksville, USA.
Pierina Morales OviedoDentistry, A.T. Still University, Kirksville, USA.
Yoley C Paredes SaavedraDentistry, Universidad Nacional Experimental de los Llanos Centrales Rómulo Gallegos, San Juan de los Morros, VEN.
Eleazar Garcia AlvarezDentistry, Universidad Central de Venezuela, Caracas, VEN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The pursuit of a perfect smile has become a prominent concern among young adults, and teeth are now read as markers of attractiveness, youth, and social belonging rather than as purely functional structures. Instagram and TikTok sit at the center of this shift, exposing large audiences every day to content on teeth whitening, veneers, smile design, and orthodontics. This narrative review, organized through the Population, Concept, Context framework, examines how these platforms shape esthetic dental trends and patient expectations, and it argues that their value is real but conditional on professional mediation. The trends that circulate most widely include the Hollywood smile, built with porcelain or composite veneers, whitening, esthetic orthodontics, gummy smile correction, and tooth ornamentation. Alongside genuine benefits, wider access to oral health information, stronger patient engagement, and legitimate educational reach, the review identifies unrealistic expectations, misinformation, body image disturbance, demand for irreversible procedures, and an ethical gap around misleading before-and-after imagery and undisclosed sponsorship. The clinician emerges as the decisive variable, since the same platforms that distort expectations can be redirected toward evidence-based education when practitioners manage expectations, screen for body dysmorphic signals, secure informed consent, and uphold advertising ethics.

Indexed as

body dysmorphic disorderdental ethicsdigital smile designesthetic dentistryinstagrampatient expectationssocial mediatiktok

Identifiers

PMID42610032
PMCPMC13480293

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.