Pressure Garment Therapy After Burn Injury
Bibliographic record
Abstract
Standard care for the prevention and treatment of abnormal scarring after burn injury includes pressure garment therapy (PGT); however, clinical trials underlying this recommendation have been small, underpowered, and of poor methodological quality. To determine the efficacy of PGT compared to control for the prevention and treatment of abnormal scarring after burn injury. Randomized control trials (RCTs) were identified from CINHAL, EMBASE, MEDLINE, CENTRAL, the “grey literature” and hand searching of the Proceeding of the American Burn Association. Primary authors and pressure garment manufacturers were contacted to identify eligible trials. Bibliographies from included studies and reviews were searched. Included studies were limited to RCTs of patients with burn wounds, treated with PGT or no pressure garment therapy. Two reviewers independently selected articles for inclusion and assessed methodological quality. Two reviewers independently extracted data; the primary outcomes was Vancouver Scar Scale. Missing data were obtained from authors or calculated from other data presented in the paper. The data were analyzed using the Cochrane Review Manager 4.2.3. Studies were pooled to yield weighted mean differences (WMD), standardized mean difference (SMD) or odds ratios (OR) and reported using 95% confidence intervals (95% CI).
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".