Comparison of IFSG to SOC for treatment of venous leg ulcers using real-world data from the USWR with 1:1 matching on 14 wound/patient factors
Bibliographic record
Abstract
ABSTRACT Background Venous leg ulcers (VLUs) impose substantial morbidity and Medicare spending, yet many real-world ulcers remain refractory to standard of care (SOC). Intact fish-skin graft (IFSG) is a biologic graft used for chronic wounds. We evaluated the comparative effectiveness of IFSG versus SOC in routine practice using a specialty wound registry with Real World Evidence design features intended to minimize bias. Methods We performed a retrospective, target-trial–emulating, 1:1 propensity score–matched comparative-effectiveness study within the U.S. Wound Registry (USWR). Matching used 14 prespecified patient- and wound-level covariates (including mobility as a measure of frailty and number of concomitant wounds). Results The matched cohort included 129 IFSG-treated VLUs and 129 SOC-treated VLUs. Baseline balance was excellent by standardized mean differences. Small residual differences favored SOC; IFSG-treated wounds were older and trended larger. Healing occurred in 85.3% of IFSG-treated wounds (110/129) versus 75.2% of SOC-treated wounds (97/129); the absolute difference (+10.1%) was just below statistical significance (p=0.0801). SOC-treated VLUs increased in size on average more than IFSG-treated VLUs (p=0.0036). Conclusions In a national wound registry with rigorous cohort construction, aligned index timing, comprehensive covariate control, and structured outcome capture, IFSG demonstrated favorable real-world effectiveness versus SOC for VLUs with a trend towards more healed wounds and a statistically significant lower average wound expansion. The high healing rate in the SOC arm is plausibly explained by baseline advantages (shorter duration, smaller area, and “never-advanced-therapy” selection) as well as the absence of a set follow-up duration that typically extended until healing, a competing event, or administrative end of observation.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".