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This research was funded by the Spanish Ministry of Science and Innovation, the OASIS project (Grant PID2020-113222RB-C21) and the OASIS-T project (Grant PID2020-113222RB-C22).

Analysis of institutional authors

Fernández-Carnero, JosuéCorresponding AuthorArribas-Romano, AlbertoAuthor
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Article

Prediction of Patient Satisfaction after Treatment of Chronic Neck Pain with Mulligan's Mobilization

Publicated to:Life. 13 (1): 48- - 2023-01-01 13(1), DOI: 10.3390/life13010048

Authors: Fernandez-Carnero, Josue; Beltran-Alacreu, Hector; Arribas-Romano, Alberto; Cerezo-Tellez, Ester; Cuenca-Zaldivar, Juan Nicolas; Sanchez-Romero, Eleuterio A; Lara, Sergio Lerma; Villafane, Jorge Hugo

Affiliations

IRCCS Fdn Don Carlo Gnocchi, Piazzale Morandi 6, I-20148 Milan, Italy - Author
Primary Hlth Ctr El Abajon, Calle Principado Asturias 30, Las Rozas 28231, Spain - Author
Puerta Hierro Hlth Res Inst Segovia Arana IDIPHIS, Res Grp Nursing & Hlth Care, Manuel Falla S-N, Majadahonda 28220, Spain - Author
Rey Juan Carlos Univ, Int Doctoral Sch, Mostoles 28933, Spain - Author
Univ Alcala, Fac Med & Ciencias Salud, Dept Enfermeria & Fisioterapia, Grp Invest Fisioterapia & Dolor, Alcala De Henares 28801, Spain - Author
Univ Autonoma Madrid, Ctr Super Estudios Univ La Salle, Dept Phys Therapy, Madrid 28023, Spain - Author
Univ Autonoma Madrid, Ctr Super Estudios Univ La Salle, Inst Neurosci & Sci Movement INCIMOV, Mot Brains Res Grp, Madrid 28023, Spain - Author
Univ Castilla La Mancha, Fac Phys Therapy & Nursing, Toledo Physiotherapy Res Grp GIFTO, Ave Carlos III S-N, Toledo 45071, Spain - Author
Univ Europea Canarias, Fac Hlth Sci, Dept Physiotherapy, Santa Cruz De Tenerife 38300, Spain - Author
Univ Europea Canarias, Fac Hlth Sci, Musculoskeletal Pain & Motor Control Res Grp, C Inocencio Garcia 1, La Orotava 38300, Spain - Author
Univ Europea Madrid, Fac Sport Sci, Dept Physiotherapy, Villaviciosa De Odon 28670, Spain - Author
Univ Europea Madrid, Fac Sport Sci, Musculoskeletal Pain & Motor Control Res Grp, Madrid 28670, Spain - Author
Univ La Salle, CranioSPain Res Grp, Ctr Super Estudios, Calle Salle 10, Madrid 28023, Spain - Author
Univ Rey Juan Carlos, Dept Phys Therapy Occupat Therapy Rehabil & Phys, Alcorcon 28922, Spain - Author
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Abstract

Chronic neck pain is among the most common types of musculoskeletal pain. Manual therapy has been shown to have positive effects on this type of pain, but there are not yet many predictive models for determining how best to apply manual therapy to the different subtypes of neck pain. The aim of this study is to develop a predictive learning approach to determine which basal outcome could give a prognostic value (Global Rating of Change, GRoC scale) for Mulligan's mobilization technique and to identify the most important predictive factors for recovery in chronic neck pain subjects in four key areas: the number of treatments, time of treatment, reduction of pain, and range of motion (ROM) increase. A prospective cohort dataset of 80 participants with chronic neck pain diagnosed by their family doctor was analyzed. Logistic regression and machine learning modeling techniques (Generalized Boosted Models, Support Vector Machine, Kernel, Classsification and Decision Trees, Random Forest and Neural Networks) were each used to form a prognostic model for each of the nine outcomes obtained before and after intervention: disability-neck disability index (NDI), patient satisfaction (GRoC), quality of life (12-Item Short Form Survey, SF-12), State-Trait Anxiety Inventory (STAI), Beck Depression Inventory (BDI II), pain catastrophizing scale (ECD), kinesiophobia-Tampa scale of kinesiophobia (TSK-11), Pain Intensity Visual Analogue Scale (VAS), and cervical ROM. Pain descriptions from the subjects and pain body diagrams guided the physical examination. The most important predictive factors for recovery in chronic neck pain patients indicated that the more anxiety and the lower the ROM of lateroflexion, the higher the probability of success with the Mulligan concept treatment.

Keywords
Artificial-intelligenceBeck depression inventoryCervical rangeChronic neck painChronic painLow-back-painMachine learningManual therapyMulligan conceptMusculoskeletal manipulationsPsychometric propertiesReliabilitySpanish versionThoracic spineValidationVisual analog

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Life due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 26/109, thus managing to position itself as a Q1 (Primer Cuartil), in the category Biology.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.52. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 3.02 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 5.03 (source consulted: Dimensions May 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-05-13, the following number of citations:

  • WoS: 11
  • Scopus: 13
  • Europe PMC: 5
  • Google Scholar: 2
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-13:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 76.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 76 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 2.5.
  • The number of mentions on the social network X (formerly Twitter): 3 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Italy.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Fernández Carnero, Josué) .

the author responsible for correspondence tasks has been Fernández Carnero, Josué.