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Record W3173697246 · doi:10.1177/12034754211027515

Laser Resurfacing Monotherapy for the Treatment of Actinic Keratosis

2021· review· en· W3173697246 on OpenAlexaff
Felicia Tai, Monica Shah, Kucy Pon, Afsáneh Alavi

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2021
Typereview
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineActinic keratosisAblative caseDermatologyDermisLesionActinic keratosesSurgeryKeratolyticPathologyRadiation therapy

Abstract

fetched live from OpenAlex

Treating actinic keratosis (AK) and photodamaged skin is critical to reduce the risk of progression to skin cancer. Laser resurfacing for AK treatment is available as either lesion-directed or field therapy. Laser resurfacing removes the superficial epidermis and dermis containing actinic damage, promoting re-epithelialization of healthy skin. Although laser resurfacing has been explored as a modality for AK treatment in the literature, studies summarizing its efficacy in the treatment of AK are lacking. This review summarizes existing research on laser resurfacing as a monotherapy for AK treatment, highlighting the various laser resurfacing modalities available for AK treatment as well as their complications and efficacy in comparison to other therapies. Despite longer healing time, fully ablative laser resurfacing, including carbon dioxide and erbium-doped yttrium aluminum garnet were found to be more effective for AK treatment than fractional ablative techniques. Although some studies suggest laser resurfacing monotherapy as less efficacious than photodynamic therapy, and equally effective to 5-fluorouracil and 30% trichloroacetic acid, clinical trials of larger sample size are required to establish stronger evidence-based conclusions. Moreover, laser resurfacing used as lesion-directed therapy, as opposed to the usual field-therapy, requires further investigation.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
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 head0.136
GPT teacher head0.395
Teacher spread0.259 · 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 designOther design
Domainnot available
GenreReview

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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

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