Emergency Room Arteriography: An Updated Digital Technology
Bibliographic record
Abstract
BACKGROUND: Emergency room arteriography (ERA) is a safe, accurate, simple and cost-effective method of defining arterial injuries. Limitations include the difficulty of evaluating limb vasculature distal to the suspected site of injury. Statscan is a novel, low-dose digital X-ray machine that can rapidly obtain a whole body image in a single scan. Our goal was to evaluate the role of Statscan technology in ERA. METHODS: A 24 month retrospective review of all patients who underwent a Statscan assisted ERA at the Groote Schuur Hospital Trauma Unit was completed. Indications for ERA included a hemodynamically stable patient with hard signs of a vascular injury in conjunction with the clinical assessment of a threatened limb. Contraindications encompassed instability, massive bleeding or a rapidly expanding hematoma. RESULTS: Ten patients underwent Statscan assisted ERA of their lower limbs. Eight had cold, pulseless limbs with impaired neurological examinations. Common femoral, superficial femoral and popliteal artery lacerations were displayed. Three patients had no identifiable injury and were observed. Seven patients underwent operative management for threatened limbs. Two had Statscan evidence of arterial emboli distal to the site of injury leading to further exploration and distal embolectomy. CONCLUSIONS: Statscan ERA is safe, rapid, simple and accurate. It has the advantage of providing arteriography distal to the site of injury. This directly altered patient care in 20% of cases, primarily by detecting distal arterial emboli. Thirty percent of patients with normal ERA also avoided an unnecessary operation. This study demonstrates a new role for Statscan technology.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".