Hand-Held Thoracic Sonography for Detecting Post-Traumatic Pneumothoraces: The Extended Focused Assessment With Sonography For Trauma (EFAST)
Bibliographic record
Abstract
BACKGROUND: Thoracic ultrasound (EFAST) has shown promise in inferring the presence of post-traumatic pneumothoraces (PTXs) and may have a particular value in identifying occult pneumothoraces (OPTXs) missed by the AP supine chest radiograph (CXR). However, the diagnostic utility of hand-held US has not been previously evaluated in this role. METHODS: Thoracic US examinations were performed during the initial resuscitation of injured patients at a provincial trauma referral center. A high frequency linear transducer and a 2.4 kg US attached to a video-recorder were used. Real-time EFAST examinations for PTXs were blindly compared with the subsequent results of CXRs, a composite standard (CXR, chest and abdominal CT scans, clinical course, and invasive interventions), and a CT gold standard (CT only). Charts were reviewed for in-hospital outcomes and follow-up. RESULTS: There were 225 eligible patients (207 blunt, 18 penetrating); 17 were excluded from the US examination because of battery failure or a lost probe. Sixty-five (65) PTXs were detected in 52 patients (22% of patients), 41 (63%) being occult to CXR in 33 patients (14.2% whole population, 24.6% of those with a CT). The US and CXR agreed in 186 (89.4%) of patients, EFAST was better in 16 (7.7%), and CXR better in 6 (2.9%). Compared with the composite standard, the sensitivity of EFAST was 58.9% with a likelihood ratio of a positive test (LR+) of 69.7 and a specificity of 99.1%. Comparing EFAST directly to CXR, by looking at each of 266 lung fields with the benefit of the CT gold standard, the EFAST showed higher sensitivity over CXR (48.8% versus 20.9%). Both exams had a very high specificity (99.6% and 98.7%), and very predictive LR+ (46.7 and 36.3). CONCLUSION: EFAST has comparable specificity to CXR but is more sensitive for the detection of OPTXs after trauma. Positive EFAST findings should be addressed either clinically or with CT depending on hemodynamic stability. CT should be used if detection of all PTXs is desired.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".