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Record W3187426325 · doi:10.21203/rs.3.rs-519510/v1

Point-of-Care Ultrasound Curriculum in Acute Care Medicine: A Protocol for a Systematic Review and/or Meta-Analysis

2021· review· en· W3187426325 on OpenAlexaff
Leon Byker, Brian Buchanan, Jocelyn Slemko, Irene Ma, Jason Weacher, Robin Featherstone, Megan Sebastianski, Oleksa Rewa

Bibliographic record

VenueResearch Square · 2021
Typereview
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsProtocol (science)Meta-analysisPoint of care ultrasoundMedicineCurriculumPoint of careAcute careMedical physicsPoint (geometry)Intensive care medicineUltrasoundAlternative medicineHealth carePsychologyNursingInternal medicineRadiologyPathologyPolitical sciencePedagogyMathematics

Abstract

fetched live from OpenAlex

Abstract BackgroundBeginning with the guidance of central line insertion, point-of-care ultrasound (POCUS) has evolved into a more inclusive skill set to aid in the examination and management of the acutely ill patient. Published evidence, including original literature and consensus recommendations support an array of applications in the multi-disciplinary arena of acute care medicine. In parallel, we have seen multiple professional societies’ call for more POCUS training in residency. While POCUS has been received with enthusiasm in acute care medicine, there are a number of challenges to ensuring trainees can competently perform POCUS in the acute care environment. There is inconsistent evidence to support optimum practices in curriculum design, implementation, assessment, and evaluation. To help explore this gap, we are conducting a systematic review and meta-analysis of current evidence regarding POCUS curricula.MethodsWe will search electronic databases: MEDLINE, Embase, Cochrane Library, CINAHL, Ovid ERIC, Science Citation Index, and Conference Proceedings Citation Index. Further, we will search the ClinicalTrials.gov register, hand search key proceedings and check references from relevant systematic reviews. Title, abstract and full text screening for inclusion of eligible papers will be performed in duplicate, in accordance with the PRISMA statement. Included publications will be evaluated for internal validity using the Medical Education Research Study Quality Instrument (MERSQI) scale for educational studies. Data abstraction will be conducted using standardized forms with focus on learner population, number of participants, setting, POCUS application, methods of instruction, duration of intervention, methods of assessment, and program evaluation. Further to this, emphasis will be placed on validity arguments of assessment tools using Kane’s framework. Primary analysis will be qualitative in nature. When possible, homogenous studies will be pooled for quantitative meta-analysis.DiscussionOur systematic review will summarize the current evidence base for POCUS curriculum implementation, evaluation and assessment validity for acute care applications. We anticipate that our review will fill a critical knowledge gap, providing a sound platform for future evidence-based curriculum development.Systematic Review RegistrationOur systematic review was registered with the International prospective register of systematic reviews (PROSPERO) on September 19, 2018 with registration number: CRD42018105973.

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)0.068
metaresearch head score (Gemma)0.108
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.068
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.108
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0320.032
Bibliometrics0.0140.012
Science and technology studies0.0030.003
Scholarly communication0.0080.008
Open science0.0050.004
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0630.005

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 head0.265
GPT teacher head0.584
Teacher spread0.319 · 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
GenreProtocol

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

Citations0
Published2021
Admission routes1
Has abstractyes

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