Point of Care Ultrasound Literature Primer: Key Papers on Focused Assessment With Sonography in Trauma (FAST) and Extended FAST
Bibliographic record
Abstract
Objective The objective of this study is to identify the top five most influential papers published on focused assessment with sonography in trauma (FAST) and the top five most influential papers on the extended FAST (E-FAST) in adult patients. Methods An expert panel was recruited from the Canadian Association of Emergency Physicians (CAEP) Emergency Ultrasound Committee and the Canadian Ultrasound Fellowship Collaborative. These experts are ultrasound fellowship-trained or equivalent, are involved with point-of-care ultrasound (POCUS) research and scholarship, and are leaders in both the POCUS program at their local site and within the national Canadian POCUS community. This 14-member expert group used a modified Delphi process consisting of three rounds of sequential surveys and discussion to achieve consensus on the top five most influential papers for FAST and E-FAST. Results The expert panel identified 56 relevant papers on FAST and 40 relevant papers on E-FAST. After completing all three rounds of the modified Delphi process, the authors identified the top five most influential papers on FAST and the top five most influential papers on E-FAST. Conclusion We have developed a reading list of the top five influential papers for FAST and E-FAST that will benefit residents, fellows, and clinicians who are interested in using POCUS in an evidence-informed manner.
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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 | 0.078 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.037 | 0.026 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".