Ultrasound-assisted brace casting for adolescent idiopathic scoliosis, IRSSD Best research paper 2014
Bibliographic record
Abstract
BACKGROUND: Brace treatment is the most effective non-surgical treatment for AIS. High initial in-brace correction increases successful brace treatment outcomes. The objective of this study was to investigate if real-time ultrasound (US) can aid orthotists in selecting the pad pressure level and location resulting in optimal in-brace correction of the spine. METHODS: Twenty six AIS subjects participated in this pilot study with 17 (2 M, 15 F) in the control group and 9 (2 M, 7 F) in the intervention group. For the control group, the standard method was used to design their braces. In addition to the standard of care, a medical 3D ultrasound (US) system, a custom pressure measurement system and in-house software were used to select pad placement and pressure levels for the intervention group. The orthotist used a custom standing Providence brace design system to apply pressures against the patient's torso. The applied pad pressures were recorded. A real-time US spinal image was displayed. Cobb angle measurements from the baseline and the assessment scan were performed. The orthotist then decided if an adjustment was needed in terms of altering the pad locations and pressure levels. The procedures may be repeated until the orthotist attained the best simulated in-brace correction configuration to cast the brace. RESULTS: In the control group, 8 of 17 (47%) subjects needed a total of 16 brace adjustments after initial fabrication requiring a total of 33 in-brace radiographs. For the intervention group, the orthotist tried additional configurations in 7 out of 9 cases (78%). Among these 7 revised cases, 5 showed better stimulated in-brace corrections and were subsequently used to cast the brace. As a result, only 1 subject required a minor adjustment after initial fabrication. The total number of in-brace radiographs in the intervention group was 10. CONCLUSIONS: The use of the 3D ultrasound system provided a radiation-free method to determine the optimum pressure level and location to obtain the best stimulated in-brace correction during brace casting. The average number of radiographs per subject taken prior to final brace implementation with the interventional group was significantly lower than the control group.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".