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Record W4365443811 · doi:10.1002/lsm.23662

Treatment of burn hypertrophic scar with fractional ablative laser‐assisted drug delivery can decrease levels of hyperpigmentation

2023· article· en· W4365443811 on OpenAlexaboutno aff
Sanjana Kurup, Taryn E Travis, Rahma Abd El Shafy, Jeffrey W. Shupp, Bonnie C Carney

Bibliographic record

VenueLasers in Surgery and Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsHyperpigmentationMedicineAblative caseScarsMelasmaDermatologyHypertrophic scarDepigmentationLaser treatmentSurgeryLaserRadiation therapy

Abstract

fetched live from OpenAlex

Abstract Background Laser treatments have been used to treat a variety of scar symptoms, including the appearance of scars following burn injury. One such symptom is hyperpigmentation. There are several qualitative and quantitative measures of assessing improvement in hyperpigmentation over time. The Patient and Observer Scar Assessment Scale (POSAS) and Vancouver Scar Scale (VSS) are two scales that describe characteristics of scar such as pigmentation level. These scales are limited by their qualitative nature. On the other hand, spectrophotometers provide quantitative measures of pigmentation. Prior studies have reported that laser can change scar pigmentation, but no quantitative values have been reported. The current study examines changes in scar melanin index after CO 2 fractional ablative laser scar revision (FLSR) via noninvasive probe measurement in patients of various Fitzpatrick skin types (FST). Materials and Methods Patients with scars of various sizes and etiologies were treated with FLSR. A database was constructed including 189 patients undergoing laser treatment. From this pool, individuals were selected based on the criteria that they completed at least two laser sessions and had Melanin index measurements for both of these sessions and the pre‐operative visit. This criteria resulted in 63 patients of various FST in the cohort. Melanin index, POSAS‐Observer (O) and ‐Patient (P) pigmentation and color scores and VSS‐pigmentation scores were examined over time. Demographic information (age of patient at time of first treatment, age of scar at time of first treatment, use of laser‐assisted drug delivery (LADD), gender, FST, and Ethnicity) were collected from the medical record. Patients were grouped as “responder” if their Melanin index indicated decreased levels of hyperpigmentation after FLSR treatment in more than half of their total number of visits and “nonresponder” if it did not. Results The majority of patients were responders (41/63). In responder patients, measurements of Melanin index showed significantly improved levels of hyperpigmentation in hypertrophic scars after two FLSR sessions ( p < 0.05). Age of patient, gender, FST, age of scar, ethnicity, or type of drug delivered by LADD did not predict responder grouping. POSAS‐O and ‐P pigmentation/color scores showed improved scores after two FLSR sessions within the responder group. POSAS‐P color scores showed improved scores after two and three FLSR sessions in the nonresponder group. VSS pigmentation scores showed improved scores after three FLSR sessions in the responder group only. Conclusion Based on Melanin index values, FLSR leads to improvements in hyperpigmentation in certain patients.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.323
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2023
Admission routes1
Has abstractyes

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