Accuracy of Intermittent Fluoroscopy to Detect Intravascular Injection During Transforaminal Epidural Injections
Bibliographic record
Abstract
STUDY DESIGN: Prospective validity study. OBJECTIVE: To determine how accurately intermittent fluoroscopy detects inadvertent intravascular injection during transforaminal epidurals. SUMMARY OF BACKGROUND DATA: Serious morbidity caused by transforaminal epidural injections is frequently related to inadvertent vascular injection of corticosteroids. Several methods have been proposed to reduce the risk of vascular injection, but none have demonstrated efficacy. Because of the fleeting appearance of vascular contrast patterns, live fluoroscopy is recommended during contrast injection. Despite this, many practitioners continue to use intermittent fluoroscopy. METHODS: During 50 epidural injections dynamic contrast flow was observed under live fluoroscopy, and the "dynamic true" image was determined. Two intermittent fluoroscopy images were saved from each injection, the first just before completing the contrast injection ("static C" image), and another 1 second after the contrast injection ceased ("static PC" image). Five physicians with experience performing these injections independently interpreted the 100 randomly ordered static images. Accuracy of intermittent fluoroscopy was determined by comparing the interpretation of the 100 static images with the dynamic true patterns observed under live fluoroscopy. RESULTS: Overall, interpretation of the static images missed 57% of the vascular injections. Timing of the static images influenced accuracy with the static C images missing 50% of vascular injections, and the static PC images missing 68% of vascular injections (P = 0.075). Accuracy was significantly worse when vascular injections occurred simultaneous to the expected epidural injection (P = 0.041), and in lumbar images (P = 0.012). CONCLUSION: Based on these findings, we recommend use of live fluoroscopy to observe dynamic contrast flow during transforaminal epidural steroid injections.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".