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Record W4416344558 · doi:10.24908/pocusj.v10i02.19092

SpacED POCUS: A Randomized Controlled Trial of an Adaptive Spaced Education POCUS Curriculum for Medical Students

2025· article· en· W4416344558 on OpenAlexvenueno aff
Anelah McGinness, S.L. Hancock, Megan Hilbert, Jane Soung, Emily Lovallo

Bibliographic record

VenuePOCUS Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
FundersUniversity of Pittsburgh
KeywordsRandomized controlled trialTest (biology)CurriculumEducational measurementUltrasound

Abstract

fetched live from OpenAlex

This study evaluated the effectiveness, retention, engagement, and acceptability of adaptive spaced education (spacED) for improving the accuracy of point of care ultrasound (POCUS) image interpretation by medical students. From July 2022-May 2023, students (n=36) were randomized into two groups and each assigned 50 unique POCUS cases: cardiac/vascular or lung/FAST. Each one served as the control for the other group. We measured effectiveness (% posttest 1 - % pretest), six-month retention (% posttest 2 - % posttest 1), engagement (% cases completed), and acceptability (% would recommend). Twenty-nine students (81%) completed the study. On average, 38.6% of cases were completed over the six-month study period. There was a significant increase in test scores covering FAST (Focused Assessment with Sonography in Trauma) (+18%), lung (+25%), and vascular (+23%, all p<0.01). Six-month FAST and lung scores did not have significant loss (+3% and -10%, p >0.05). Acceptability was high; 96% of students indicated they would participate again. Despite an imperfect case completion rate, for some applications, spacED was an effective, long-lasting, and acceptable method for teaching POCUS interpretation to medical students.

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)0.006
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.408
Teacher spread0.393 · 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 teacher head, not a consensus.

Study designRandomized trial
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

Citations0
Published2025
Admission routes1
Has abstractyes

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