Evaluating outcomes of pulsed dye laser therapy combined with intralesional triamcinolone injection after surgical removal of hypertrophic cesarean section scars
Bibliographic record
Abstract
BACKGROUND: Recently, pulsed dye laser (PDL) combined with triamcinolone intralesional injection (TAILI) has been introduced for surgical scar prevention. However, little is known about this procedure's effectiveness in preventing hypertrophic scar following surgical scar removal. OBJECTIVES: This study aimed to evaluate the outcome of early intervention using PDL combined with TAILI after surgical removal of hypertrophic cesarean section (CS) scars. METHODS: The medical records of 35 patients who underwent early intervention using PDL and TAILI after removal of hypertrophic CS scars were retrospectively reviewed. The scars' average Vancouver Scar Scale (VSS) scores before scar removal and 3 months after the final treatment were compared. RESULTS: The patients received 4.23 treatments on average and were followed up for a mean period of 7.74 months. The mean final VSS was 3.11 ± 1.52 and was significantly lower than that of the previous VSS (9.29 ± 1.74, p = 0.000). VSS of the previous CS scar, and the presence or absence of keloid formation in other areas, was associated with treatment outcome (p = 0.003 and 0.008, respectively). CONCLUSIONS: Early intervention using PDL combined with TAILI could prevent the recurrence or progression of hypertrophic CS scarring after surgical scar removal.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".