Detection of soft tissue foreign bodies by nurse practitioner-performed ultrasound
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: This study aimed to evaluate the accuracy of emergency nurse practitioner (NP)-performed point-of-care ultrasound (POCUS) for the detection of soft tissue foreign bodies (FBs). METHODS: Following a 2-h training session, ten NPs were assessed on their ability to detect various FBs in an experimental model. FBs (wood, metal and plastic) were inserted randomly into eight experimental models (uncooked chicken thighs) by an independent observer. Control experimental models had no FB inserted, but all had a 1-cm incision made on their surface. NPs, blinded to the type of model, were then assessed on their ability to detect the FBs by ultrasound examination using high-frequency linear transducers (Toshiba Nemio). Models were also scanned by two experienced emergency physicians (EPs) as a further control. RESULTS: Overall, NP-performed POCUS detected 47 of the 60 foreign bodies with a sensitivity, specificity, positive predictive value and negative predictive value of 78.3%, 50%, 82% and 43%, respectively, compared with 83.3%, 75%, 90.9% and 60% for EPs. Sensitivity for detecting specific types of FB was 95%, 85% and 50% for wood, metal and plastic, respectively, for NP-performed POCUS, compared with 100%, 100% and 50% in the EP group. CONCLUSIONS: NPs with no previous ultrasound experience can detect soft tissue FBs with accuracy comparable to that of EPs in an experimental model. Test sensitivity was high for wood and metal foreign bodies. Specificity was generally low.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it