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Record W4411041930 · doi:10.3389/fdgth.2025.1555733

Development, implementation and evaluation of a digital treatment for adolescents with chronic pain: a protocol for a multi-phase study

2025· article· en· W4411041930 on OpenAlexaff
Jordi Miró, Ariadna Sampietro, Sónia Monterde, Pablo Ingelmo, Rikard K. Wicksell, C. Nolla, Mercedes Alonso, Juan José Lázaro, E. Martínez García, Armando Sánchez, Vanessa Sánchez Mendoza, Álvaro Vázquez, Rocío de la Vega, Francisco Reinoso‐Barbero

Bibliographic record

VenueFrontiers in Digital Health · 2025
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill UniversityMcGill University Health CentreMontreal Children's Hospital
FundersEuropean Regional Development FundMinisterio de Ciencia e InnovaciónGeneralitat de CatalunyaAgència de Gestió d'Ajuts Universitaris i de RecercaFundación Grünenthal EspañaInstitució Catalana de Recerca i Estudis Avançats
KeywordsPsychosocialUsabilityChronic painProtocol (science)Intervention (counseling)Health careFocus groupQuality of life (healthcare)Physical therapyMedicineDigital healthPsychologyAlternative medicineNursingPsychiatryComputer science

Abstract

fetched live from OpenAlex

Chronic pain in adolescents is an increasing public health concern with a significant physical, psychological, and social impact. This study aims to develop, implement, and evaluate DigiDOL-Ad, a digital psychosocial treatment for adolescents with chronic pain, supplemented by dedicated websites for their parents and teachers. This multicenter study will be conducted in four phases: (1) Development of the intervention framework and foundational planning; (2) Focus groups with adolescents with chronic pain, their parents, teachers, healthcare professionals and health authorities to identify specific needs and tailor the psychosocial treatment and related components; (3) Iterative usability testing of the digital treatment, using an a hermeneutical circle methodology to refine the design based on participant feedback; and (4) Evaluation of DigiDOL-Ad through a pre-treatment, post-treatment, and 3-month follow-up assessment. DigiDOL-Ad has the potential to improve the quality of life for adolescents with chronic pain. By leveraging digital health technologies, this innovative approach could establish a new benchmark for treating adolescents with chronic pain, emphasizing interdisciplinary and stakeholder-driven care. Clinical Trial Registration: clinicaltrials.gov, identifier NCT06765200.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.530
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.078
GPT teacher head0.463
Teacher spread0.385 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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