Adaptation of a MR imaging protocol into a real-time clinical biometric ultrasound protocol for persons with spinal cord injury at risk for deep tissue injury: A reliability study
Bibliographic record
Abstract
BACKGROUND: High strain in soft tissues that overly bony prominences are considered a risk factor for pressure ulcers (PUs) following spinal cord impairment (SCI) and have been computed using Finite Element methods (FEM). The aim of this study was to translate a MRI protocol into ultrasound (US) and determine between-operator reliability of expert sonographers measuring diameter of the inferior curvature of the ischial tuberosity (IT) and the thickness of the overlying soft tissue layers on able-bodied (AB) and SCI using real-time ultrasound. MATERIAL AND METHODS: Part 1: Fourteen AB participants with a mean age of 36.7 ± 12.09 years with 7 males and 7 females had their 3 soft tissue layers in loaded and unloaded sitting measured independently by 2 sonographers: tendon/muscle, skin/fat and total soft tissue and the diameter of the IT in its short and long axis. Part 2: Nineteen participants with SCI were screened, three were excluded due to abnormal skin signs, and eight participants (42%) were excluded for abnormal US signs with normal skin. Eight SCI participants with a mean age of 31.6 ± 13.6 years and all male with 4 paraplegics and 4 tetraplegics were measured by the same sonographers for skin, fat, tendon, muscle and total. Skin/fat and tendon/muscle were computed. RESULTS: AB between-operator reliability was good (ICC = 0.81-0.90) for 3 soft tissues layers in unloaded and loaded sitting and poor for both IT short and long axis (ICC = -0.028 and -0.01). SCI between-operator reliability was good in unloaded and loaded for total, muscle, fat, skin/fat, tendon/muscle (ICC = 0.75-0.97) and poor for tendon (ICC = 0.26 unloaded and ICC = -0.71 loaded) and skin (ICC = 0.37 unloaded and ICC = 0.10). CONCLUSION: A MRI protocol was successfully adapted for a reliable 3 soft tissue layer model and could be used in a 2-D FEM model designed to estimate soft tissue strain as a novel risk factor for the development of a PU.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".