Primary failure of thoracic epidural analgesia: revisited
Bibliographic record
Abstract
Primary failure of thoracic epidural analgesia (TEA) remains an important clinical problem, whose incidence can exceed 20% in teaching centers. Since loss-of-resistance (LOR) constitutes the most popular method to identify the thoracic epidural space, the etiology of primary TEA failure can often be attributed to LOR's low specificity. Interspinous ligamentous cysts, non-fused ligamenta flava, paravertebral muscles, intermuscular planes, and thoracic paravertebral spaces can all result in non-epidural LORs. Fluoroscopy, epidural waveform analysis, electrical stimulation, and ultrasonography have been proposed as confirmatory modalities for LOR.The current evidence derived from randomized trials suggests that fluoroscopy, epidural waveform analysis, and possibly electrical stimulation, could decrease the primary TEA failure to 2%. In contrast, preprocedural ultrasound scanning provides no incremental benefit when compared with conventional LOR. In the hands of experienced operators, real-time ultrasound guidance of the epidural needle has been demonstrated to provide comparable efficacy and efficiency to fluoroscopy.Further research is required to determine the most cost-effective confirmatory modality as well as the best adjuncts for novice operators and for patients with challenging anatomy. Moreover, future trials should elucidate if fluoroscopy and electrical stimulation could potentially decrease the secondary failure rate of TEA, and if a combination of confirmatory modalities could outperform individual ones.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".