Longitudinal Evaluation of Pressure Applied by Custom Fabricated Garments Worn by Adult Burn Survivors
Bibliographic record
Abstract
Custom fabricated pressure garments (PGs) are commonly used to prevent or treat hypertrophic scars (HSc) after burn injury. However, there is minimal scientific evidence quantifying pressure after standard measurement and fitting techniques. Adult burn survivors whose HSc was treated with PGs were recruited. Trained fitters, blinded to study locations and results, took the garment measures. Once the PGs arrived and were fitted, baseline pressure measures at HSc and normal skin (NS) sites were determined using the Pliance X® System. Pressure readings were repeated at 1, 2, and 3 months. The mean baseline pressure was 15.3 (SD 10.4) at HSc and 13.4 (SD 11.9) at NS sites. There was a significant reduction during the first month at both sites (P = .0002 HSc; P = .0002 NS). A multivariable linear regression mixed model, adjusting for garment type, baseline pressure, and repeated measures, revealed further reduction at HSc sites between 1 and 2 months (P = .03). By 3 months, the mean pressure reduced to 9.9 (SD 6.7) and 9.15 (SD 7.2) mm Hg at HSc and NS sites, respectively. At each time point, the pressure was higher at HSc compared with NS but was significantly different only at 1 month (P = .01). PGs were worn ≥12 hr/d 7 d/wk. PGs that apply 15 to 25 mm Hg pressure significantly improve HSc; however, immediately after fitting newly fabricated PGs, the average pressure was at the bottom of the recommended range and by 1 month was significantly below. Clinicians are likely underestimating the dosage required and the significant pressure loss within the first 2 months.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".