Prospective Study of Intense Pulsed Light With Postoperative Radiotherapy for Keloids in Young and Middle‐Aged Men
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy of using a combination of intense pulsed light (IPL) with a 500-600 nm wavelength filter and postoperative radiotherapy to treat chest scars in young and middle-aged men. METHODS: A prospective randomized controlled trial was conducted with 52 male patients. Those receiving postoperative radiotherapy combined with IPL were assigned to the observation group, while those receiving only postoperative radiotherapy were assigned to the control group. Follow-ups were conducted from 0 to 24 months to assess efficacy using the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS). RESULTS: No significant differences in VSS or POSAS scores were observed prior to treatment (p > 0.05). Following treatment, however, the observation group demonstrated significantly superior outcomes (p < 0.001), exhibiting lower scores in terms of color, vascular distribution, softness and total VSS (p < 0.05), though thickness scores remained unchanged (p > 0.05). Pain and itchiness scores were also significantly lower in the observation group. CONCLUSION: Combining postoperative radiotherapy with IPL is more effective than radiotherapy alone at improving chest scars, and significantly alleviates pain and itching symptoms in young and middle-aged men.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".