Beyond Ultrasound Guidance for Regional Anesthesiology
Bibliographic record
Abstract
Despite its popularity, ultrasound (US)-guided regional anesthesiology is associated with significant limitations. The latter can be attributed to either the US machine (ie, decreased ability to insonate deep neural structures, as well as the thoracic spine) or the operator. Shortcomings associated with the operator can be explained by errors in perception (ie, ambiguous criteria for needle/catheter tip-to-nerve proximity and subparaneural local anesthetic injection) or interpretation. Perhaps the greatest confusion afflicting US-guided regional anesthesiology originates from an intellectual misconception pertaining to its application. Increasingly, authors are using US to identify interfascial planes where local anesthetic can be injected thereby "discovering" new truncal blocks. Often these novel blocks suffer from a lack of proper randomized, comparative validation.Fortunately, solutions have been proposed to remedy many shortcomings associated with US guidance. The inability of US to reliably insonate deep neural structures can be circumvented with adjunctive neurostimulation. Fluoroscopy and waveform analysis have been proven to increase the success rate of thoracic epidural blocks. For continuous nerve blocks, combined US-neurostimulation may provide an objective end point (ie, an evoked motor response) for neural proximity and subparaneural positioning of the catheter tip. Finally, the solution to the plethora of nonvalidated US-guided blocks is both elegant and simple. New nerve blocks should answer a specific clinical need, and their first descriptions should take the form of an adequately powered, observer-blinded, randomized comparison against the established standard of care or, at the very least, a large case series (eg, a Brief Technical Report).
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 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".