Localization of epidural space: A review of available technologies
Bibliographic record
Abstract
Although epidural analgesia is widely used for pain relief, it is associated with a significant failure rate. Loss of resistance technique, tactile feedback from the needle, and surface landmarks are traditionally used to guide the epidural needle tip into the epidural space (EDS). The aim of this narrative review is to critically appraise new and emerging technologies for identification of EDS and their potential role in the future. The PubMed, Cochrane Central Register of Controlled Clinical Studies, and Web of Science databases were searched using predecided search strategies, yielding 1048 results. After careful review of abstracts and full texts, 42 articles were selected to be included. Newer techniques for localization of EDS can be broadly classified into techniques that (1) guide the needle to the EDS, (2) identify needle entry into the EDS, and (3) confirm catheter location in EDS. An ideal method should be easy to learn and perform, easily reproducible with high sensitivity and specificity, identifies inadvertent intrathecal and intravascular catheter placements with ease, feasible in perioperative setting and have a cost-benefit advantage. Though none of them in their current stages of development qualify as an ideal method, many show tremendous potential. Some techniques are useful in patients with difficult spinal anatomy and infants, and thus are complementary to traditional methods. In addition to improving the existing technology, future research should aim at proving the superiority of these techniques over traditional methods, specifically regarding successful EDS localization, better safety profile, and a favorable cost-benefit ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".