A Randomized Comparison Between Conventional and Waveform-Confirmed Loss of Resistance for Thoracic Epidural Blocks
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Epidural waveform analysis (EWA) provides a simple confirmatory adjunct for loss of resistance (LOR): when the needle tip is correctly positioned inside the epidural space, pressure measurement results in a pulsatile waveform. In this randomized trial, we compared conventional and EWA-confirmed LOR in 2 teaching centers. Our research hypothesis was that EWA-confirmed LOR would decrease the failure rate of thoracic epidural blocks. METHODS: One hundred patients undergoing thoracic epidural blocks for thoracic surgery, abdominal surgery, or rib fractures were randomized to conventional LOR or EWA-LOR. The operator was allowed as many attempts as necessary to achieve a satisfactory LOR (by feel) in the conventional group. In the EWA-LOR group, LOR was confirmed by connecting the epidural needle to a pressure transducer using a rigid extension tubing. Positive waveforms indicated that the needle tip was positioned inside the epidural space. The operator was allowed a maximum of 3 different intervertebral levels to obtain a positive waveform. If waveforms were still absent at the third level, the operator simply accepted LOR as the technical end point. However, the patient was retained in the EWA-LOR group (intent-to-treat analysis).After achieving a satisfactory tactile LOR (conventional group), positive waveforms (EWA-LOR group), or a third intervertebral level with LOR but no waveform (EWA-LOR group), the operator administered a 4-mL test dose of lidocaine 2% with epinephrine 5 μg/mL. Fifteen minutes after the test dose, a blinded investigator assessed the patient for sensory block to ice. RESULTS: Compared with LOR, EWA-LOR resulted in a lower rate of primary failure (2% vs 24%; P = 0.002). Subgroup analysis based on experience level reveals that EWA-LOR outperformed conventional LOR for novice (P = 0.001) but not expert operators. The performance time was longer in the EWA-LOR group (11.2 ± 6.2 vs 8.0 ± 4.6 minutes; P = 0.006). Both groups were comparable in terms of operator's level of expertise, depth of the epidural space, approach, and LOR medium. In the EWA-LOR group, operators obtained a pulsatile waveform with the first level attempted in 60% of patients. However, 40% of subjects required performance at a second or third level. CONCLUSIONS: Compared with its conventional counterpart, EWA-confirmed LOR results in a lower failure rate for thoracic epidural blocks (2% vs 24%) in our teaching centers. Confirmatory EWA provides significant benefits for inexperienced operators.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".