Abstracts from the Veterinary Emergency and Critical Care Ultrasound Society
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: Studies have shown cardiovascular veterinary pointof-care ultrasound (VPOCUS) performed by non-specialists helps differentiate cardiac from respiratory disease, and that a short handson training course including interpretation of cineloops improves novice sonographer cardiac VPOCUS accuracy. Studies evaluating sonographer interpretation of LUS cineloops in companion animals are lacking. This study evaluated the accuracy of novice sonographer interpretation of LUS using a binary question approach over a 3-month period. We hypothesized that hands-on VPOCUS training and cineloops interpretation will increase novice sonographer accuracy to answer binary LUS questions. Materials and methods: Twelve interns, with minimal prior ultrasound experience, received a 5-h (1 theory, 4 practical) course on LUS, using a binary question approach. Learner performance to assess LUS findings was assessed prior to (T0), immediately following (T1), and 3 months after training (T3). Between T1 and T3 interns had access to scan clinical patients using VPOCUS, and to record cineloops for review by an experienced VPOCUS clinician. Results: The accurate/inaccurate/unanswered (mean (SD)) responses to binary LUS questions increased from 36.3% (12.8)/15.3% (4.1)/48.4% (13.3) at T0 to 64.6% (9.2)/10.7% (2.8)/24.7% (11.3) at T1 to 85.9% (5.8)/9.8% (3.4)/4.3% (6.2) at T3, respectively. Accuracy for detection of pleural effusion, b-line presence, and b-line quantification was 67.4% (2.6), 74.31% (3.1), and 71.5% (3.1) at T0. Accuracy for the curtain sign, Z lines, lung point, shred sign, double curtain sign, and I-lines was lower at 29.2% (1.4), 22.9% (0.9), 16.7% (0.8), 6.9% (0.4), 4.9% (0.6), and 2.1% (0.4), respectively. At T1, the accuracy of detecting curtain signs, Z lines, double curtain and lung point increased to > 50%, but remained low for I-lines (7.6% (0.9)) and the shred sign (18.1% (1)), with 80% of novices leaving I line and shred sign questions unanswered. At T3 all binary questions were accurately answered > 75% of the time, with > 90% accuracy for double curtain sign (98.6% (0.4)), pleural effusion (93.8% (1.5)), shred sign (93.8% (0.9)), and curtain sign (91% (1.2)). Conclusions: Novice sonographers can rapidly answer most binary questions on LUS with high accuracy following a brief hand on training session and 3 months of clinical practice. Given the difficulty of identifying I-lines and the shred sign, these may be areas requiring greater training. Capture and interpretation of cineloops during clinical practice, with feedback from an experienced VPOCUS operator, appears to improve novice sonographer learner performance rapidly.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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