Early Patient‐Reported Outcomes as Predictors of Long‐Term Scar Satisfaction: An Exploratory Cohort Study
Bibliographic record
Abstract
ABSTRACT Early prediction of long‐term scar outcomes is essential for guiding clinical decision‐making and improving patient satisfaction. This study investigates whether 3‐month patient‐reported outcomes (PROs) using SCAR‐Q domains—psychosocial, appearance and quality of life (QoL)—predict 12‐month outcomes, and how these relate to objective scar measures. A prospective cohort of 20 female patients undergoing various surgical procedures completed SCAR‐Q and Patient and Observer Scar Assessment Scale (POSAS) evaluations at 3 and 12 months postoperatively. Correlation and linear regression analyses assessed associations and predictive validity between early and late scar outcomes. SCAR‐Q QoL scores demonstrated strong predictive validity ( R 2 = 0.49, p < 0.001; ρ = 0.70, p < 0.001), whereas psychosocial and appearance domains showed weak, nonsignificant associations ( R 2 = 0.12 and 0.10, respectively; p > 0.1). Objective scar characteristics—particularly width and height—were significantly correlated with poorer 12‐month appearance and psychosocial scores (e.g., ρ = −0.743 for height vs. appearance, p < 0.001; ρ = −0.605 for width vs. psychosocial, p = 0.0047). In point‐biserial correlations, wider and taller scars at 3 months were more likely to be rated as ‘bad’ at 12 months ( r ≥ |0.53|, p ≤ 0.016). POSAS and overall opinion scores also significantly improved over time ( p < 0.05), but some patients reported increased appearance‐related distress despite objective improvements. In conclusion, early QoL assessments reliably predict long‐term outcomes, while appearance and psychosocial perceptions may shift over time. These findings support routine use of PROs in early postoperative care to inform personalised interventions and optimise scar management.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".