Real-Time Ultrasound-Guided Spinal Anaesthesia: A Prospective Observational Study of a New Approach
Bibliographic record
Abstract
Identification of the subarachnoid space has traditionally been achieved by either a blind landmark-guided approach or using prepuncture ultrasound assistance. To assess the feasibility of performing spinal anaesthesia under real-time ultrasound guidance in routine clinical practice we conducted a single center prospective observational study among patients undergoing lower limb orthopaedic surgery. A spinal needle was inserted unassisted within the ultrasound transducer imaging plane using a paramedian approach (i.e., the operator held the transducer in one hand and the spinal needle in the other). The primary outcome measure was the success rate of CSF acquisition under real-time ultrasound guidance with CSF being located in 97 out of 100 consecutive patients within median three needle passes (IQR 1-6). CSF was not acquired in three patients. Subsequent attempts combining landmark palpation and pre-puncture ultrasound scanning resulted in successful spinal anaesthesia in two of these patients with the third patient requiring general anaesthesia. Median time from spinal needle insertion until intrathecal injection completion was 1.2 minutes (IQR 0.83-4.1) demonstrating the feasibility of this technique in routine clinical practice.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".