An objective measurement of brace usage for the treatment of adolescent idiopathic scoliosis
Bibliographic record
Abstract
Effectiveness of orthotic treatment for scoliosis depends on how much time and how well the orthosis is worn. Questionnaires and clinical judgment are subjective methods to wear compliance. Even though using a temperature sensor can objectively record how long the orthosis has been used, it may not be able to answer the orthosis effectiveness without knowing the wear tightness. Custom made thoracolumbosacral orthoses (TLSO) were instrumented with low power wireless data acquisition systems to measure the time and loads imposed by the pressure pad during daily activities. Force measurements were recorded at 1 sample/min and the system was able to record data up to 4 months without patient-involvement. Ten subjects (9F, 1M), age between 9 and 13.5 years, average 11.6±1.3 years, who prescribed a new TLSO and full-time brace wear were took part in this study over 4.4±1.0 months. Long-term logging of loads within a spinal orthosis is a reliable method to measure compliance objectively. The monthly quantity of brace wear ranged from 33% to 82%, average 60.0±4.3%. The monthly average loads imposed by the pressure pads varied from 39% to 78% relative to the reference level, average 64.3±4.6%. There was a statistically significant decrease in force, but increase in wear time over the period after the brace fitting session. This information may help to better understand the effectiveness of bracing and to predict the brace treatment outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".