The Accuracy of Locating Lumbar Vertebrae When Using Palpation Versus Ultrasonography
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the accuracy of locating lumbar vertebrae using palpation vs ultrasonography. METHODS: In this study, ultrasonic imaging was used by 2 experienced clinicians to identify the third lumbar spinous process (target) of a female participant. The target was then located by 16 undergraduate chiropractic students using clinical palpation techniques learned in their academic program (with participant seated and prone) and ultrasonic imaging learned through a 5-minute training video. Presumed target locations identified by students were recorded by infrared motion capture equipment. The coordinates of the presumed target site were then compared statistically. RESULTS: There was no significant difference between the presumed target position identified by the students using sitting and prone palpation (P = .346). These positions were significantly different from the target location identified by expert clinicians using ultrasonic imaging (P < .0001 in both cases). The vertebra identified by ultrasonic imaging by the students was the same vertebra identified by the expert clinicians using ultrasound. This position error in the vertebra identified by palpation resulted in the students mistakenly identifying the L4 spinous process as the target vertebra. CONCLUSIONS: This study found that ultrasonography provided more accurate identification of a lumbar spinal landmark when compared with palpation. In addition, our data suggest that ultrasonic imaging to identify spinal landmarks can be learned easily and can improve accuracy of landmark detection. Although the time to use ultrasonic imaging was greater than with palpation, these results suggest that this procedure could potentially be used in clinical practice to identify spinal landmarks.
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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 | 0.005 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".