An early stage brace wear pattern during daily activities for AIS
Bibliographic record
Abstract
To evaluate changes in compliance including both wear tightness and wear time during early brace treatment for AIS. The efficacy of brace treatment for children with AIS has been hampered by the lack of comprehensive information about wear characteristics. Our group developed a reliable brace compliance monitoring system to measure and record the temporal profile of the loads on the pressure pad imposed on the trunk during daily living. The brace compliance monitoring system was used to monitor how new brace subjects used their braces during first 4 months. Six AIS subjects (5 F, 1 M), between 10 and 13 years old (12.3 + 1.0 years), prescribed TLSO with full time wear (22 hours per day) were monitored starting at the beginning of their brace treatment. The Cobb angles were measured at the initial visit, 4 weeks after the final brace fitting (in-brace) and the first follow-up visit (out-of-brace) approximately 4 months after initiation. The force average relative to the prescribed tightness level (set as 1.0) and the monthly force comparison were reported. The average wear time and monthly wearing pattern were calculated. The brace monitor logged the data for 4 months without any data loss. The initial, the in-brace and the follow-up Cobb angles were 33 ± 4, 21 ± 3, and 35 ± 5 degrees, respectively. During this study period, the daily force average relative to the prescribed level was 0.97 ± 0.20. The average force from month 1 to 4 was 1.12 ± 0.23, 1.02 ± 0.20, 0.92 ± 0.18, 0.83 ± 0.19, respectively. The average wear time relative to the prescribed time was 56 ± 15%. The monthly wear time from month 1 to 4 were 52 ± 8.6, 54 ± 13, 59 ± 16, 59 ± 21%, respectively. All subjects are still on their brace treatment. During the first 4 months of brace use, the wear time improves but brace tightness is lower.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 |
| 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".