Identification of the Lumbar Interspinous Spaces: Palpation Versus Ultrasound
Bibliographic record
Abstract
In Brief BACKGROUND: Palpation has been shown to be inaccurate at identifying lumbar interspinous spaces. Our goal in this study was to compare ultrasound imaging of the region to palpation. METHODS: Using ultrasound in the postpartum period, we estimated the interspinous level used for obstetric neuraxial anesthesia in 121 women and compared this estimation with the level estimated by palpation and documented in the chart by the anesthesiologist. RESULTS: In 67 of 121 (55%) patients, the vertebral level of the puncture mark documented by the treating anesthesiologist was in agreement with vertebral level as assessed using ultrasound, and in 39 (32%) women, the skin puncture level was estimated by ultrasound to be at least one interspace higher. The unweighted kappa was 0.08 (95% confidence interval: 0.02, 0.14). CONCLUSIONS: There was poor agreement between palpation and ultrasound estimation of the specific lumbar interspace, and when there was disagreement, the ultrasound estimate was more often higher than the palpitation estimate. IMPLICATIONS: This study compared the lumbar interspinous space estimated by palpation during obstetric neuraxial anesthesia to that estimated by ultrasound examination. There was poor agreement between the two methods, and when there was disagreement, the ultrasound estimate was more often higher than the palpitation estimate.
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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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".