The Accuracy of Ultrasonic Indentation in Detecting Simulated Bone Displacement: A Comparison of Three Techniques
Bibliographic record
Abstract
PURPOSE: Palpation is used most commonly to assess tissue stiffness despite its well-known deficiencies. As an improvement, a mechanical technique known as ultrasonic indentation has been proposed. The purpose of this study was to compare the accuracy of 3 ultrasonic indentation techniques in quantifying bone displacement in a specially constructed tissue simulator. METHODS: Three ultrasonic indentation techniques were tested for their accuracy: a rigid, laboratory-based method (rigid), a less rigid system actuated by hand (assisted), and a totally free-hand system (handheld). Each indentation technique was applied on a tissue simulator, which consisted of a deformable phantom overlying a displaceable piston to simulate soft tissue overlying bone. Measures of piston (ie, bone) displacement obtained by each indentation technique were compared with a gold standard of piston displacement to determine the accuracy of each technique. Statistical tests were used to determine if differences between experimental and reference measures of piston displacement were significant. RESULTS: When indented, phantom deformation preceded piston displacement because of unequal stiffness between the two. The rigid and assisted indentation techniques showed the best accuracy for measuring simulated bone displacement. Differences in accuracy between the rigid and assisted techniques were insignificant. The accuracy of the handheld technique was significantly less than the rigid and assisted techniques. CONCLUSIONS: The clinical utility of assisted ultrasonic indentation should be explored given its accuracy and the excessive size, cost, and complexity of the rigid technique. The large error magnitude of the handheld technique may exclude it from clinical use now.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".