A Novel Ultrasound Software System for Lumbar Level Identification in Obstetric Patients
Bibliographic record
Abstract
(Can J Anaesth. 2022;69:1211–1219) Manual palpation is the primary method that obstetric anesthesiologists use to identify the optimal point of neuraxial anesthesia placement. As it is unreliable, especially when performed in patients with a higher body mass index (BMI), lumbar ultrasound (LUS) is an intriguing option. LUS for neuraxial labor anesthesia and analgesia has improved accuracy in lumbar vertebral level identification and improved first-pass success rate. Despite potential LUS benefits, many physicians continue using manual palpation because they are not proficient in the technical skills required to perform or interpret LUS images. To make LUS simpler and more user-friendly, automated ultrasound software known as Spine Level Identification (SLIDE) system was developed. SLIDE automatically identifies vertebral landmarks in real-time as the ultrasound transducer moves over the skin. This study aimed to determine whether SLIDE accurately identifies the L3-L4 intervertebral space in healthy term parturient women compared with the manual palpation method.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".