The Telemedicine-Based Pediatric Examination of the Back and Lower Limbs: A Narrative Review
Bibliographic record
Abstract
Constantinos Dean Noutsios,1 Virginie Boisvert-Plante,1 Erika Laberge,2 Jordi Perez,3,4 Pablo Ingelmo2,4,5 1Faculty of Medicine, McGill University, Montreal, QC, Canada; 2Edward’s Family Interdisciplinary Centre for Pediatric Complex Pain, Montreal Children’s Hospital (McGill University Health Centre), Montreal, QC, Canada; 3Alan Edwards Pain Management Unit, Montreal General Hospital (McGill University Health Centre), Montreal, QC, Canada; 4Alan Edwards Centre for Pain Research, McGill University, Montreal, QC, Canada; 5Research Institute, McGill University Health Centre, Montreal, QC, CanadaCorrespondence: Pablo Ingelmo Email pablo.ingelmo@mcgill.caAbstract: The COVID-19 pandemic has accelerated the transition to virtual healthcare while also prompting an abundance of new literature highlighting telemedicine’s capabilities and limitations for various medical applications, notably musculoskeletal examinations. Telemedicine provides an opportunity to deliver timely patient- and family-centred care while maintaining physical distancing and improving access to remote communities. This review aims to narrate the current state of the literature on telemedicine applied in the context of a musculoskeletal examination for children aged 3 to 18 years. The PubMed and ScienceDirect databases were searched for relevant articles from January 2015 to August 2021 using a combination of keywords and nested searches. The general examination components relevant to the back and lumbosacral spine, hip, knee, ankle/foot, and gait are described. These components include inspection, palpation, range of motion, motor, and sensory examination as well as special testing. There is general feasibility, validity, and substantial reliability in performing most examination components, and primary diagnoses established virtually were found to be either the same or similar in the vast majority of cases. Despite the current literature focusing mainly on adult populations, we describe how each aspect of the exam can be reliably incorporated into a virtual appointment specific to the pediatric population. Currently available smartphone-based applications that measure joint range of motion were generally found to have high reliability and validity. Caregivers are needed for most of the consultation, especially in younger children, but select physical exam maneuvers can be self-performed by older children and adolescents alone. By providing an overview of the available smartphone tools as well as the reliability and validity of remote assessments, this review not only establishes a foundation for a structured pediatric musculoskeletal examination, but also aims to increase providers’ confidence in incorporating telemedicine into their practice.Keywords: telehealth, telemedicine, eHealth, pediatric, musculoskeletal, evaluation
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".