The Telemedicine-Based Pediatric Examination of the Neck and Upper Limbs: A Narrative Review
Bibliographic record
Abstract
Abstract: With the COVID-19 pandemic hastening the adoption of telemedicine into clinical practice, it has also prompted an abundance of new literature highlighting its capabilities and limitations. The purpose of this review is to summarize the current state of the literature on telemedicine applied in the context of a musculoskeletal examination of the neck and upper limbs for children 3 to 18 years old. The PubMed and ScienceDirect databases were searched for relevant articles from January 2015 to August 2021 using a combination of keywords and nested searches. General examination components including inspection, guided self-palpation, range of motion, sensory and motor examination, as well as special testing are described. Although the literature is focused mainly on adult populations, we describe how each component of the exam can be reliably incorporated into a virtual appointment specific to pediatric patients. Caregivers are generally needed for most consultations, but certain maneuvers can be self-performed by older children and adolescents alone. There is general feasibility, validity, and substantial reliability in performing most examination components of the upper limbs remotely, except for the shoulder exam. Compared to those made in person, clinical diagnoses established virtually were found to be either the same or similar in most cases, and management decisions also had high agreement. Despite this, there is evidence that some pediatric providers may not be able to collect all the information needed from a telemedicine visit to make a complete clinical assessment. Lastly, currently available smartphone applications measuring joint range of motion were found to have high reliability and validity. This narrative review not only establishes a foundation for a structured pediatric musculoskeletal examination, but also aims to increase physicians’ confidence in incorporating telemedicine into their standard of care. Keywords: telehealth, telemedicine, eHealth, pediatric, musculoskeletal, evaluation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".