Utilization Criteria for Prehospital Ultrasound in a Canadian Critical Care Helicopter Emergency Medical Service: Determining Who Might Benefit
Bibliographic record
Abstract
Introduction Prehospital ultrasound (PHUS) assessments by physicians and non-physicians are performed on medical and trauma patients with increasing frequency. Prehospital ultrasound has been shown to be of benefit by supporting interventions. Problem Which patients may benefit from PHUS has not been clearly identified. METHODS: A multi-variable logistic regression analysis was performed on a previously created retrospective dataset of five years of physician- and non-physician-performed ultrasound scans in a Canadian critical care Helicopter Emergency Medical Service (HEMS). For separate medical and trauma patient groups, the a-priori outcome assessed was patient characteristics associated with the outcome variable of "PHUS-supported intervention." RESULTS: Both models were assessed (Likelihood Ratio, Score, and Wald) as a good fit. For medical patients, the characteristics of heart rate (HR) and shock index (SI) were found to be most significant for an intervention being supported by PHUS. An extremely low HR was found to be the most significant (OR=15.86 [95% confidence interval (CI), 1.46-171.73]; P=.02). The higher the SI, the more likely that an intervention was supported by PHUS (SI 0.9 to<1.3: OR=9.15 [95% CI, 1.36-61.69]; P=.02; and SI 1.3+: OR=8.37 [95% CI, 0.69-101.66]; P=.09). For trauma patients, the characteristics of Prehospital Index (PHI) and SI were found to be most significant for PHUS support. The greatest effect was PHI, where increasing ORs were seen with increasing PHI (PHI 14-19: OR=13.36 [95% CI, 1.92-92.81]; P=.008; and PHI 20-24: OR=53.10 [95% CI, 4.83-583.86]; P=.001). Shock index was found to be similar, though, with lower impact and significance (SI 0.9 to<1.3: OR=9.11 [95% CI, 1.31-63.32]; P=.025; and SI 1.3+: OR=35.75 [95% CI, 2.51-509.81]; P=.008). CONCLUSIONS: In a critical care HEMS, markers of higher patient acuity in both medical and trauma patients were associated with occurrences when an intervention was supported by PHUS. Prospective study with in-hospital follow-up is required to confirm these hypothesis-generating results. O'Dochartaigh D , Douma M , Alexiu C , Ryan S , MacKenzie M . Utilization criteria for prehospital ultrasound in a Canadian critical care Helicopter Emergency Medical Service: determining who might benefit. Prehosp Disaster Med. 2017;32(5):536-540.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".