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Record W3041553626 · doi:10.1097/corr.0000000000001388

Are Virtual Fracture Clinics During the COVID-19 Pandemic a Potential Alternative for Delivering Fracture Care? A Systematic Review

2020· review· en· W3041553626 on OpenAlexaboutno aff
Evelyn P. Murphy, Christopher Fenelon, Robert Murphy, Michael O’Sullivan, Eoghan Pomeroy, Eoin Sheehan, D. Moore

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2020
Typereview
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTelemedicineTriageMEDLINETelehealthSystematic reviewHip fractureHealth careMedical emergencyIntervention (counseling)PandemicPatient safetyCoronavirus disease 2019 (COVID-19)Physical therapyFamily medicineDiseaseNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Virtual fracture clinics are an alternative to the traditional model of fracture care. Since their introduction in 2011, they have become increasingly used in the United Kingdom and Ireland. The coronavirus disease 2019 (COVID-19) health crisis has driven institutions to examine such innovative solutions to manage patient care. The current controversies include quantifying safety outcomes, such as potential delayed or missed injuries, inadequate treatment, and medicolegal claims. Questions also exist regarding the potential for cost reductions and efficiencies that may be achieved. Physical distancing has limited the number of face-to-face consultations, so this review was conducted to determine if virtual fracture clinics can provide an acceptable alternative in these challenging times. QUESTIONS/PURPOSES: The aim of this systematic review was to describe (1) adverse outcomes, (2) cost reductions, and (3) efficiencies associated with the virtual fracture clinic model. METHODS: A systematic review of the PubMed, MEDLINE, and Embase databases was conducted from database inception to March 2020. The keywords "virtual" or "telemedicine" or "telehealth" or "remote" or "electronic" AND "fracture" or "trauma" or "triage" AND "clinic" or "consultation" were entered, using the preferred reporting items for systematic reviews and meta-analyses. Inclusion criteria included adults and children treated for injuries by a virtual clinic model at the initial review. Eligible injuries included injuries deemed to not need surgical intervention, and those able to be treated remotely using defined protocols. Exclusion criteria consisted of patients reviewed by telemedicine using video links or in person at the initial review. Initially, 1065 articles were identified, with 665 excluded as they did not relate to virtual fracture clinics. In all, 400 articles were screened for eligibility, and 27 full-text reviews were conducted on 18 studies (30,512 virtual fracture clinic encounters). Three subdomains focusing on adverse outcomes, cost reductions, and efficiencies were recorded. The term adverse outcomes was used to describe any complications, further surgeries, re-referrals back to the clinic, or deviations from the protocols. Efficiency described the number of patients reviewed and discharged using the model, savings in clinic slots, reduced waiting times, or a reduction in consumption of resources such as radiographs. All studies were observational and the quality was assessed using Newcastle-Ottawa tool, which demonstrated a median score of 6 ± 1.8, indicating moderate quality. RESULTS: Six studies reported adverse outcomes in detail, with events ranging from inappropriate splinting, deviations from protocols, and one patient underwent an osteotomy for a malunion. Efficiency varied from direct discharge proportions of 18% in early studies to 100% once the virtual fracture clinic model was more established. Cost reductions compared with estimates derived from conventional fracture clinics varied from USD 53 to USD 297 and USD 39,125 to USD 305876 compared with traditional fracture clinic visits. CONCLUSIONS: Virtual fracture clinics may provide a means to treat patients remotely, using agreed-upon protocols. They have an important role in the current COVID-19 pandemic, due to the possibility to provide ongoing care in an otherwise challenging setting. More robust studies looking at this model of care will be needed to assess its long-term effects on patients, institutions, and health care systems. LEVEL OF EVIDENCE: Level IV, therapeutic study.

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)0.005
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.688
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.008
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 head0.157
GPT teacher head0.500
Teacher spread0.343 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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

Citations74
Published2020
Admission routes1
Has abstractyes

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