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Record W4410122490 · doi:10.24908/pocusj.v10i01.18072

What Lies Beneath? Using Point of Care Ultrasound (POCUS) to Identify Soft Tissue Foreign Bodies in Children and Adults: A Literature Review

2025· review· en· W4410122490 on OpenAlexvenueno aff
David McCreary, B Sarvesh, Mian Munir

Bibliographic record

VenuePOCUS Journal · 2025
Typereview
Languageen
FieldMedicine
TopicForeign Body Medical Cases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCINAHLMEDLINESystematic reviewProspective cohort studyRadiological weaponCohort studySoft tissuePathologySurgeryPsychological intervention

Abstract

fetched live from OpenAlex

Objective: We aimed to evaluate and appraise the existing evidence on the use of point of care ultrasound (POCUS) for identifying soft tissue foreign bodies (FBs). Methods: We searched PubMed, Medline, CINAHL, and Cochrane databases for prospective and retrospective studies evaluating the reliability of POCUS in identifying soft tissue FBs. Our primary intention was to review the paediatric-specific evidence base. However, due to a paucity of literature in this area, we also included relevant adult studies and case reports. Results: We identified a total of 42 unique articles with relevance to our study objective, of which 3 were paediatric cohort studies and 5 were cohort studies involving paediatric patients. There were two paediatric case series and six individual case reports relating to paediatric patients. The remaining studies either involved adults, did not specify the age of their subjects, or were relevant in-vitro studies. Conclusion: POCUS-users regard it as an effective tool for detecting soft tissue FBs. However, the existing evidence base for POCUS use in paediatric patients is limited. Evidence in adults is also relatively lacking compared with other areas of POCUS research, with few large studies evaluating its reliability. This literature review highlights the need for a large prospective paediatric study in order to confirm its effectiveness compared to traditional radiological imaging.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.018
GPT teacher head0.368
Teacher spread0.350 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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