갑상선 수술 후 발생한 비대흉터와 켈로이드에서 스테로이드 병변내 주사 단일요법과 스테로이드 병변내 주사 및 냉동치료 병용요법에 관한 비교 연구
Bibliographic record
Abstract
Background: Various treatment modalities for hypertrophic scars and keloids have been used. However, there is no consensus as to what the optimum approach should be. Most common treatments are corticosteroid intralesional injection (ILI) and cryotherapy as well as combination of these two modalities. To this date, however, there are no prospectively comparative, scar-split studies between steroid ILI monotherapy and combination of steroid ILI and cryotherapy. Objective: The purpose of this article is to investigate and compare the efficacy of steroid ILI monotherapy and the combination of steroid ILI and cryotherapy. Methods: Eighteen women who had thyroid operation scars were recruited. Patients received steroid ILI with cryotherapy on the right half, and steroid ILI monotherapy on the left half of the scar. Patients were treated for four sessions with three weeks of intervals. Subjects were evaluated on their scar status with the modified Vancouver scar scale (MVSS) and scar redness by using colorimeter at baseline and every visit day. Results: After four treatment sessions, MVSS was significantly improved on both sides of scar. Significant improvement was observed after one treatment session on the right half, and after two treatment sessions on the left half. There was no significant difference between left and right side after four sessions of treatment. The scar redness of both sides of scar showed no significant differences between the baseline and at the end of the study. Conclusion: Both corticosteroid ILI with cryotherapy and corticosteroid ILI monotherapy are effective treatment modalities for hypertrophic scars. However, the results of the present study suggest that a combination therapy might lead to more rapid improvements. (Korean J Dermatol 2013;51(7):489∼493)
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".