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Record W3042010063 · doi:10.2196/19924

Management and Treatment of Concussions via Tele-Concussion in a Pediatric Setting: Methodological Approach and Descriptive Analysis

2020· article· en· W3042010063 on OpenAlexvenueno aff
Todd Caze, Gregory Knell, John P. Abt, Scott O. Burkhart

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthConcussionSpecialtyMedicineDescriptive statisticsSubspecialtyMedical emergencySports medicineTelemedicineFamily medicinePoison controlInjury preventionPhysical therapyHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Approximately 2 million children in the United States sustain a concussion annually, resulting in an economic impact as high as US $20 billion. Patients who receive treatment at concussion specialty clinics, versus primary care, experience faster recovery, thereby reducing patient burden and subsequent medical-related costs. Accessibility to specialty clinics is typically limited by the availability of in-office visits. This is particularly relevant in light of the severe acute respiratory syndrome coronavirus 2 pandemic and subsequent guidance to eliminate all non-medically necessary in-clinic visits. Telehealth has been used to effectively deliver in-clinic care across several disciplines including psychiatry, psychology, and neuropsychology. However, a model of telehealth delivered concussion assessment, treatment, and management has not been established. OBJECTIVE: The purposes of this paper are to describe a pediatric concussion specialty clinic's experiences in delivering telehealth concussion services and to provide preliminary descriptive data on a sample of pediatric telehealth patients with concussions. METHODS: The specialty pediatric concussion clinic described here began providing telehealth services in 2019 and is part of the largest and fastest-growing telehealth hospital network in the United States. The clinical care process will be described, including accessing the telehealth platform, assessment during the initial appointment, injury management including communication with relevant patient stakeholders (eg, parent or guardians, athletic trainers), dissemination of rehabilitation exercises, and nature of follow-up visits. Descriptive data will include patient demographics, the radius of care, the time between the date of injury and initial visit, the average number of follow-up visits, and days until medically cleared for return-to-learn and return-to-play. RESULTS: The analytic sample included 18 patients with concussions who were seen for all of their visits via telehealth between August 2019 and April 2020. The mean age of the sample was 14.5 (SD 2.5) years. The radius of care was a median of 17 (IQR 11.0-31.0) miles from the clinic with a median time between injury and the first visit of 21 (IQR 6.0-41.5) days. The mean number of visits was 2.2 (SD 0.8) with a median days between visits of 5.4 (IQR 3.0-9.3) to manage and treat the concussion. Of the 18 patients, 55.6% (n=10) were medically cleared for return-to-learn or -play in a median of 15.5 (IQR 11.0-29.0) days. CONCLUSIONS: Limited access to health care is a well-understood barrier for receiving quality care. Subsequently, there are increasing demands for flexibility in delivering concussion services remotely and in-clinic. This is the first paper to provide a clinically relevant framework for the assessment, management, and treatment of acute concussion via telehealth in a pediatric population.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.110
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0080.010
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.175
GPT teacher head0.376
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2020
Admission routes1
Has abstractyes

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