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Record W4410932540 · doi:10.7759/cureus.85203

Patient Self-Scanning for Lung Ultrasound: A Prospective Observational Study on the Feasibility and Diagnostic Accuracy of a Telemedicine Protocol

2025· article· en· W4410932540 on OpenAlexaff
Katharine T Clark, Kathleen L. McFadden, Benjamin A Krauss, Lachlan Driver, Irene Ma, Rachel Vivian, Jamie Gullikson, Lauren Selame, Calvin Huang, Andrew S. Liteplo, Hamid Shokoohi

Bibliographic record

VenueCureus · 2025
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineObservational studyProtocol (science)TelemedicineMedical physicsLung ultrasoundUltrasoundPhysical therapyRadiologyIntensive care medicinePathologyHealth careAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the feasibility and diagnostic accuracy of patient-performed lung ultrasound (P-PLUS) for telemedicine purposes. METHODS: This prospective observation study included patients over 18 years old who presented to a tertiary care hospital's emergency department. Patients were provided a five-minute instructional video on a US protocol of four lung zones and then performed the protocol while being monitored by a study investigator. The physician sonographer subsequently repeated the protocol. Two emergency physicians with US fellowship training blindly reviewed and independently rated image quality on a scale of one to five, with a score of three or more considered interpretable. Inter-rater reliability was estimated using the intraclass correlation coefficient. Wilcoxon-Mann-Whitney tests and chi-square tests were used to compare group differences. RESULTS: A total of 56 patients (45% female) were enrolled, and 417 clips were analyzed. Ten (18%) participants worked in the medical field, and 44 (79%) had at least some college education. Forty (71%) regularly used technology at work, 52 (93%) had internet access at home, and the same number had access to smartphones. Patients reported high comfort in performing self-LUS (median score: 4, interquartile range (IQR): 3.5-5) and high willingness to perform US acquisition again in the future (median score: 4, IQR: 4-5). The proportion of interpretable images was similar between the two groups except for the left hemidiaphragm (90% of provider-obtained images were interpretable vs. 45% of patient-obtained images, P = 0.002). The majority of patient-obtained images were scored between three and four and classified as interpretable. Mean image scores were significantly higher for provider-obtained images (P < 0.05). Inter-rater reliability between the two raters was good (intraclass correlation coefficient (ICC)= 0.80, 95% CI 0.76-0.84). CONCLUSION: Patients can independently obtain interpretable LUS images in all views with minimal video-based instruction. The ability of patients to obtain interpretable LUS images with minimal tele-guidance, as well as their high levels of comfort and willingness to perform the procedure, support the potential use of P-PLUS in home-based and remote patient care.

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)0.004
metaresearch head score (Gemma)0.019
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.435
Teacher spread0.330 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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