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Record W4378070515 · doi:10.7759/cureus.39441

Clinical Roles in the Medical Communications Centre: A Rapid Scoping Review

2023· article· en· W4378070515 on OpenAlexaff
J Greene, Judah Goldstein, J. L. Stirling, Janel Swain, Ryan Brown, Jennifer McVey, Alix Carter

Bibliographic record

VenueCureus · 2023
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsDalhousie UniversityNova Scotia Health AuthorityNova Scotia Hospital
Fundersnot available
KeywordsMedicineObservational studyInclusion (mineral)Emergency departmentData extractionEmergency medical servicesProtocol (science)Scope (computer science)Medical emergencyMEDLINEScale (ratio)Alternative medicineNursingPathology

Abstract

fetched live from OpenAlex

In recent years, 911 call volumes have increased, and emergency medical services (EMS) are routinely stretched beyond capacity. To better match resources with patient needs, some EMS systems have integrated clinician roles into the emergency medical communications centre (MCC). Our objective was to explore the nature and scope of clinical roles in emergency MCCs. Using a rapid scoping review methodology, we searched PubMed for studies related to any clinical role employed within an emergency MCC. We accepted reviews, experimental and observational designs, as well as expert opinions. Studies reporting on dispatcher recognition and pre-arrival instructions were excluded. Title and abstract screening were conducted by a single reviewer, included studies were verified by two reviewers, and data extraction was completed in duplicate, all using Covidence review software. The level of evidence was assessed using the prehospital evidence-based practice (PEP) scale. The protocol was registered in Open Science Framework (10.17605/OSF.IO/NX4T8). Our search yielded 1071 titles, and four were added from other sources; 44 studies were reviewed at the full-text stage and 31 were included. The included studies were published from 2002 to 2022 and represent 17 countries. Studies meeting inclusion criteria consisted of level I (n=4, 11%), II (n=13, 37%), and III (N=6, 17%) methodologies, as well as 12 other studies (34%) with qualitative or other designs. Most of the included studies reported systems that employ nurses in the MCC (n=29, 83%). Twelve (34%) studies reported on the inclusion of paramedics in the MCC, and five (14%) reported physician involvement. The roles of these clinicians chiefly consisted of triage (n=25, 71%), advice (n=20, 57%), referral to non-emergency care (n=14, 40%), and peer-to-peer consulting (n=2, 4%). Alternative dispositions (as opposed to emergency ambulance transport) for low acuity callers included self-care, as well as referral to a general practitioner, pharmacist, or other outreach programs. There is a wide range of literature reporting on clinical roles integrated within MCCs. Our findings revealed that MCC nurses, physicians, and paramedics assist substantively with triage, advice, and referrals to better match resources to patient needs, with or without the requirement for ambulance dispatch.

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)0.050
metaresearch head score (Gemma)0.145
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.050
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.145
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0400.038
Science and technology studies0.0020.002
Scholarly communication0.0090.013
Open science0.0040.005
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.134
GPT teacher head0.475
Teacher spread0.341 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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