Helicopter emergency medical services use of thoracic point of care ultrasound for pneumothorax: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Auscultating for breath sounds to assess for pneumothorax in the helicopter emergency medical services (HEMS) settings can be extremely challenging. Thoracic point of care ultrasound (POCUS) offers a seemingly more useful visual (rather than audible) alternative. This review critically and quantitatively evaluates the use of thoracic POCUS for pneumothorax in the HEMS setting. Methods A systematic literature review with meta-analysis was conducted. Only papers reporting on patients undergoing POCUS for pneumothorax in the helicopter or pre-hospital setting were included. Primary outcome was accuracy, focusing on sensitivity and specificity. Secondary outcome was practicality. PubMed, Embase and the Cochrane Library were searched. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess validity of studies. Results Twelve studies reporting on n = 1,936 images from medical and trauma patients were included in qualitative synthesis. Studies were nearly all observational designs. Most images were acquired by nurses or paramedics who were previously novices to ultrasound. The reference standard was predominantly CT. Specificity results were unanimously precise and very high, whereas sensitivity results were imprecise and extremely variable. Meta-analysis of eight studies involving n = 1,713 images yielded pooled sensitivity 61% (95% CI: 27–87%; I2 = 94%) and pooled specificity 99% (95% CI: 98–100%; I2 = 89%). Six studies involving n = 315 images reported practicality. The highest or second highest categorisation of image quality was reported in around half of those images. Conclusion Thoracic POCUS is highly specific but has extremely variable sensitivity for pneumothorax when performed in the HEMS setting. This is from purely a diagnostic (not clinical) perspective. Sensitivity increases when only clinically significant pneumothoraces are considered. Case reports reveal thoracic POCUS can appropriately alter treatment and triage decisions, but only for a small number of patients. It appears predominantly useful in mitigating against unnecessary interventions. More research reporting patient focused outcomes is required. In the meantime, thoracic POCUS appears to offer a more appropriate visual alternative to auscultation for breath sounds when assessing for pneumothorax in the HEMS setting.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".