Traumatic Pneumothorax Detection with Thoracic US: Correlation with Chest Radiography and CT—Initial Experience
Bibliographic record
Abstract
PURPOSE: To prospectively compare the accuracy of ultrasonography (US) with that of supine chest radiography in the detection of traumatic pneumothoraces, with computed tomography (CT) as the reference standard. MATERIALS AND METHODS: Thoracic US, supine chest radiography, and CT were performed to assess for pneumothorax in 27 patients who sustained blunt thoracic trauma. US and radiographic findings were compared with CT findings, the reference standard, for pneumothorax detection. For the purpose of this study, the sonographers were blinded to the radiographic and CT findings. RESULTS: Eleven of 27 patients had pneumothorax at CT. All 11 of these pneumothoraces were detected at US, and four were seen at supine chest radiography. In the one false-positive US case, the patient was shown to have substantial bullous emphysema at CT. Sensitivity and negative predictive value of US were 100% (11 of 11 and 15 of 15 patients, respectively), specificity was 94% (15 of 16 patients), and positive predictive value was 92% (11 of 12 patients). Chest radiography had 36% (four of 11 patients) sensitivity, 100% (16 of 16 patients) specificity, a 100% (four of four patients) positive predictive value, and a 70% (16 of 23 patients) negative predictive value. CONCLUSION: In this study, US was more sensitive than supine chest radiography and as sensitive as CT in the detection of traumatic pneumothoraces.
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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 | 0.002 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".