Ultrasonic indentation: A procedure for the noninvasive quantification of force-displacement properties of the lumbar spine
Bibliographic record
Abstract
BACKGROUND: Alterations in the normal force-displacement (FD) properties of spinal tissues have been associated with specific forms of pathology, such as degenerative disk disease. Unfortunately, few current procedures exist that assess spinal FD properties in an accurate, reliable, and noninvasive manner. Consequently, the clinical relevance of the relation between spinal disorders and spinal FD properties is not fully understood. OBJECTIVE: To investigate the accuracy and reliability of spinal FD measures obtained through use of a procedure in which real-time ultrasonic imaging (ultrasonic indentation) is used during load-controlled external indentation. SETTING: McCaig Centre for Joint Injuries and Arthritis Research, University of Calgary. METHODS: The bench-top accuracy and reliability of ultrasonic indentation were assessed by cyclic indentation of a spring-mounted platform. These data were compared with criterion data derived from a materials testing machine. A porcine preparation was then used to assess the accuracy of ultrasonic indentation-generated estimates of vertebral displacement in comparison with a criterion of optically tracked displacement. In addition, previously unreported parameters relating to indentation accuracy (frame deflection and off-axis loading) were characterized. RESULTS: Reliability of ultrasonic indentation ranged between 0.99 and 1.00 (intraclass correlation coefficient). Error values in force, displacement, and stiffness ranged from 0.81% to 13.62% over varying experimental conditions. CONCLUSIONS: Ultrasonic indentation is a unique procedure that is capable of assessing, noninvasively, FD properties of spinal tissues, including vertebral displacement in the indentation plane. The results of this study suggest that ultrasonic indentation is a potentially useful technique for quantifying spinal FD properties in vivo.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".