The Outcome of Early Ablative Fractional Laser Treatment for Thyroidectomy Scars
Bibliographic record
Abstract
Background and Objectives Ablative fractional laser (AFL) systems are commonly used to treat various scars, and recent research has indicated that early treatment with AFL may have a preventive effect on scars. This study was designed to evaluate the efficacy of early treatment with a 10,600 nm carbon dioxide (CO 2 ) AFL on thyroidectomy scars and compare it to late (conventional) treatment for the same and untreated controls. Study Design/Materials and Methods We performed a prospective, evaluator‐blinded, split‐scar study on fresh thyroidectomy scars between July 2014 and July 2017. Scars were divided into two equal portions. Early AFL treatment had begun 1 month after surgery; five sessions on the right half of the scar was performed at 1‐month intervals. Late AFL treatment followed for 1 month after the final early treatment session on the left half of the scar at the same interval. The scars were evaluated at 6 and 11 months postoperatively using scar analysis scales and patient questionnaires. Results Twenty‐four out of 28 patients completed the study. The mean decrease in Vancouver Scar Scale (VSS) scores was significantly higher for the early treated right halves of the scars both at the 6th month (vs. untreated controls) and at the 11th month (vs. late treated controls). The VSS subset analysis showed that the early treated sides had significantly greater improvement in pliability and height than the control sides at each point of evaluation. Conclusions Early postoperative AFL treatment is safe and effective in improving linear surgical scars, such as thyroidectomy scars, and may be a promising option for scar prevention. Lasers Surg. Med. © 2020 Wiley Periodicals, Inc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".