Fractional Ablative Carbon Dioxide Laser-Assisted Photodynamic Therapy for keloids: Clinical, Histopathological and Immunohistochemical Evaluation
Bibliographic record
Abstract
Abstract Background Keloids present a therapeutic challenge due to lack of satisfactory treatment and high rate of recurrence. Various treatment modalities exist, including intralesional steroids, photodynamic therapy (PDT), lasers and different combinations have been used to reach better results. Aim To study whether fractional ablative CO2-assisted delivery of Methylene blue-PDT is an effective method of treatment of keloids. Methods twenty patients with untreated keloids have been treated with 3 monthly sessions of fractional CO2 laser and methylene blue PDT. In the first session debulking of the keloid’s height by at least 50% with ablative CO2 was performed. Patients were photographed and assessed before treatment and 2 months after last session, clinically using modified Vancouver Scar Scale (mVSS) & patient and observer scar assessment scale (POSAS), histopathologically by H&E, Masson trichrome and immunohistochemically by TGF-b. Results total score for mVSS dropped from 8.25 ± 2.489 to 6.45 ± 2.8 (p = 0.013) after treatment, with a percentage change of -18.6 ± 39.6. Also, POSAS (observer) decreasaed from 26.90 ± 6.545 to 18.75 ± 8.045 (p = 0.001). POSAS (patient) decreased from 27.40 ± 6.644 to 19.05 ± 7.722 (p = 0.0001) Histopathologically, collagen improved from 3.45 ± 1.05to 2.65 ± 0.875. The TGFb staining increased post treatment but not significantly. Conclusions clinical and histopathological improvement was seen after MB-PDT but further controlled studies are needed to compare its effectiveness with other established modalities. Also compare results of different parameters of fractional CO2-PDT, more treatment sessions and study the long term follow up results.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".