535 Pre-operative Expectations, Post-operative Satisfaction and Patient Directed Priorities for Clinical Burn Research
Bibliographic record
Abstract
Abstract Introduction Patients receiving split thickness skin grafting due to deep burns are left with scarring and chronically dysfunctional skin at the graft site. Given evidence that patients’ pre-operative expectations mediate post-operative outcomes and satisfaction, we sought to describe burn patients’ experience, expectations, and satisfaction with their skin graft, and their views towards a future cell-based clinical trial to improve their graft, over time. We also aimed to identify graft outcome measures for use in future studies. Methods This study was approved by our university's research ethics board. All participants provided written and informed consent. Data were collected via patient questionnaires pre-operatively, one, and three months post-operatively. Results Most patients had small burns. Expectations of graft function were consistent pre- and post-operatively. Expectations of graft appearance showed significant decrease over time (β 1 = -0.290, p = 0.008). Significant improvements in skin function (β 1 = 0.579, p = 0.000) and appearance (β 1 = 0.247, p = 0.025) at the wound site during recovery were observed, although patients noted great difference between grafted and normal skin. Patient satisfaction with their graft did not change significantly over time. Patients were willing to participate in a cell-based clinical trial that may improve graft symptomology. They prioritized diminished scarring, redness, and improved sensation and elasticity as the most salient aspects of grafts to be enhanced by cell-based therapy. Conclusions Patient graft concerns changed over time; outcome measures in trials advancing skin grafting should reflect chronic, patient prioritized limitations.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".