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Enregistrement W4283810004 · doi:10.1093/asj/sjac186

Altmetric Analysis of the Most-Mentioned Articles Online in Plastic Surgery

2022· article· en· W4283810004 sur OpenAlexaboutno aff
Parth A. Patel, Carter J. Boyd

Notice bibliographique

RevueAesthetic Surgery Journal · 2022
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSocial Media in Health Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicinePlastic surgeryMEDLINEGeneral surgerySurgery

Résumé

récupéré en direct d'OpenAlex

In recent years, the corpus of scientific literature has expanded significantly. As such, bibliometrics has acquired increasing importance for identification of the most impactful publications for clinicians, scientists, and the general readership. Although historically impact has been assessed through citation count, this measure is flawed when used exclusively because it captures only a single element of scientific dissemination.1 Alternative metrics, therefore, are intended to offer insights not available through traditional metrics. Altmetric Attention Score (AAS), which is derived from a weighted algorithm designed to capture an article’s online influence from myriad sources, is one such measure.2,3 Previous analyses in plastic surgery have reported a weak correlation between AAS and citation count.1,4 However, the factors associated with obtaining the highest AAS in plastic surgery remain indeterminate. Our objective was to collate the top 100 most-mentioned articles online in plastic surgery, as defined through AAS, and examine the characteristics concomitant with greater social dissemination. From Journal Citation Reports we selected 20 journals related to plastic surgery and its subspecialties (Table 1). Altmetric Explorer was queried for papers published in relevant journals between January 1, 2015 and June 14, 2022. Citation counts provided in this database were acquired from Dimensions (London, UK), which has similar coverage to sources such as Web of Science and Scopus.5 Chi-squared test, t test, and Spearman’s ρ were conducted where appropriate. Analyses were performed via GraphPad Prism 9 (San Diego, CA), with P < 0.05 considered significant. Plastic Surgery Journals Selected for Analysis AAS, Altmetric Attention Score; IF, impact factor; NA, not applicable; SD: standard deviation. AAS of articles in the top 100 most-mentioned online, acquired via Altmetric Explorer (Altmetric, London, UK). Overall, 23,488 plastic surgery publications were identified. No types of articles were excluded in order to capture trends across all categories. Mean [standard deviation] AAS and citation count were 6.7 [31.4] and 9.1 [15.8], respectively. There were weak or minimal correlations between AAS and citation counts (ρ = 0.22; P < 0.001), citations/year (ρ = 0.25; P < 0.001), publication year (ρ = 0.06; P < 0.001), and impact factor (IF; ρ = 0.15; P < 0.001). The top 100 most-mentioned articles are detailed in Table 2. Mean AAS and citation count were 378.3 [245.5] and 36.6 [46.8], respectively. No significant correlations were observed between AAS and citation counts (ρ = –0.05; P = 0.60), citations/year (ρ = 0.06; P = 0.55), or publication year (ρ = –0.19; P = 0.05). The most frequent years of publication were 2016 and 2017 (24.0% each; Figure 1). Top 100 Most-Mentioned Articles Online in Plastic Surgery Published 2015–2022 AAS, Altmetric Attention Score. aAverage AAS acquired via Altmetric Explorer (Altmetric, London, UK). bCitation counts provided by Altmetric Explorer, which acquires data from the Dimensions database (London, UK). Trends in the number of top 100 most-mentioned articles online in plastic surgery and average AAS of those articles between January 1, 2015 and June 14, 2022. Bar graph indicates number of articles in the top 100 and line graph indicates average AAS. AAS, Altmetric Attention Score. Eleven journals were represented, although manuscripts were predominantly published by Plastic & Reconstructive Surgery (43.0%), Aesthetic Surgery Journal (24.0%), and Facial Plastic Surgery & Aesthetic Medicine (14.0%). IF was not related to AAS (ρ = 0.00; P > 0.99) or number of articles within the top 100 (ρ = 0.23; P = 0.29). Across the top 100 most-mentioned articles, 48.0% were open access (OA), a significant difference from the proportion of OA articles among all included plastic surgery papers (30.6%; P < 0.001). Relative to their non-OA counterparts, OA publications had higher AAS (all, 5.3 [26.5] vs 9.7 [40.2], P < 0.001; top 100, 329.0 [305.3] vs 423.8 [145.0], P = 0.05). First authors were overwhelmingly from the United States (79.0%), followed distantly by the United Kingdom (3.0%) and Canada (3.0%); 9.0% of manuscripts were products of multinational collaborations. When analyzed by subspecialty, articles largely pertained to aesthetic (46.0%), general (19.0%), and breast (15.0%) plastic surgery. Manuscripts were primarily original studies (61.0%), which was subdivided into cross-sectional (22.0%), retrospective observational (12.0%), and prospective observational (11.0%) analyses, among others. Our collated list of plastic surgery articles provides a snapshot of the field’s social impact. The manuscript (“Selfies—Living in the Era of Filtered Photographs”) with the highest AAS (1789) was published in Facial Plastic Surgery & Aesthetic Medicine by Rajanala et al in 2018. This editorial commented on the escalating standard of beauty wrought by technological evolution, a trend that will necessarily affect how plastic surgeons approach patient care.6 The second highest AAS (1242) belonged to an article (“Nasal Distortion in Short-Distance Photographs: The Selfie Effect”) published in Facial Plastic Surgery & Aesthetic Medicine by Ward et al in 2018. Their mathematical model delineated the distortive effects of the increasingly popular selfie on nasal size, highlighting similar considerations as Rajanala et al’s publication.7 Although the AAS represents an invaluable tool to capture an article’s impact, it is essential to note that the metric is not directly linked to an article’s scientific importance and strictly reflects the attention it garners online. Articles concerning topics that are considered popular or controversial are more likely to be disseminated on social media, irrespective of their clinical or scientific merit. Evidence of this phenomenon is observed in the 29th most-mentioned publication (“ASJ Welcomes New International Affiliate: The Hong Kong Society of Plastic, Reconstructive and Aesthetic Surgeons”), which had an AAS of 441. Despite its prolific online circulation, this editorial note’s scientific impact was nonexistent with a citation count of zero. Indeed, as indicated by the data presented herein and among other investigations,1,4 AAS possesses poor predictive power for traditional metrics. Furthermore, with the increasingly integral nature of social media to career advancement,8 there is concern authors and other parties will manipulate AAS by engaging in activities that falsely inflate visibility (buying likes, creating false accounts, etc), yet contribute minimally to scientific discourse. Finally, this metric fails to capture all social platforms, including Instagram (Meta, Menlo Park, CA) and TikTok (ByteDance, Beijing, China), which are the primary means of engagement with certain demographics.9,10 Therefore, AAS should be conceptualized as supplementary to the existing framework of research evaluation. In summary, our investigation provided the top 100 most-mentioned articles online in plastic surgery, thereby lending a perspective distinct from previous traditional bibliometric analyses in the field. The authors thank Altmetric Explorer (Altmetric, London, UK) for access to the relevant study data. The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article. The authors received no financial support for the research, authorship, and publication of this article.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,097
Score d'incertitude au seuil0,774

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,011
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,104
Tête enseignante GPT0,355
Écart entre enseignants0,251 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2022
Routes d'admission1
Résumé présentoui

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