What Lies Beneath? Using Point of Care Ultrasound (POCUS) to Identify Soft Tissue Foreign Bodies in Children and Adults: A Literature Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".