Emergency Room Arteriography: An Updated Digital Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it