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Record W4205504553 · doi:10.1080/14764172.2021.2016844

A prospective study on the treatment of immediate post-operative scar with narrowband intense pulsed light under polarized dermoscopy

2021· article· en· W4205504553 on OpenAlexaboutno aff
Qianya Su, Fei Wang, Yuanyuan Chai, Fengyuan Wang, Zhengbang Dong, Haijing Yang

Bibliographic record

VenueJournal of Cosmetic and Laser Therapy · 2021
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
FundersFundamental Research Funds for the Central Universities
KeywordsMedicineScarsVascularitySurgeryIntense pulsed lightProspective cohort studyScar tissueDermatology

Abstract

fetched live from OpenAlex

(a) To evaluate the efficacy and safety of narrow-band intense-pulsed light (DPL) in immediate post-operative scar. (b) To observe the process of scar formation under dermoscopy in the first 6 months. Nine patients with postoperative scars were enrolled in the randomized, prospective, split-scar study. Patients were treated in one half of the scar with DPL for cosmetic improvement at a wavelength of 500-600 nm and the other half was not treated as control. The laser treatments were initiated 2 weeks after the surgery and were given 3 times over a 4-week period. All patients were followed-up for 3 months from the last treatment. Photographs and dermoscopy digital images were collected each time. (a) Neither DPL or control produce statistically significant improvements in Vancouver Scar Scale. Moreover, comparatively, there was no statistical difference in Vancouver Scar Scale between DPL or control. However, 6 out of 9 patients treated with DPL had reduced scores in vascularity sooner compared with control. (b) Under dermoscopy, redness, and swelling were obvious from 2 weeks after surgery, but were gradually alleviated. The surface of the scar gradually became uneven and rough. DPL might be beneficial in early recovery of immediate post-operative scar.

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 score0.091
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.325
Teacher spread0.299 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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