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Record W2197060915 · doi:10.6000/1927-5129.2015.11.55

Different Wavelengths of LEDs on Cutaneous Wound Healing in Wistar Rats

2015· article· en· W2197060915 on OpenAlexvenueno aff
Hennes Gentil de Araújo, Rodrigo Marcel Valentim da Silva, P Canela, Nayrane de Farias Silva, Fernando Claudino dos Santos-Filho, Oscar Ariel Ronzio, Maria Goretti Freire de Carvalho, Sebastião David Santos-Filho, Patrí­cia Froes Meyer

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2015
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsLight-emitting diodeMedicineWound healingLight therapyRed lightWavelengthBlue lightSurgeryMaterials scienceOptoelectronicsBiologyInternal medicineCircadian rhythmBotany

Abstract

fetched live from OpenAlex

This study evaluates different wavelengths of LED therapy in Wistar rats skin injuries. LEDs (Light Emitting Diodes) are phototherapeutic resource nowadays, since it is considered a good alternative to Low Level Laser Therapy in injury healing because of the lower cost. Twenty-five male Wistar rats were divided in five groups: Control, Red LED (630-780 nm), Green LED (490-565 nm), Blue LED (440-490 nm) and Yellow LED (590-630 nm). It´s a experimental research that it was performed during 4 weeks. Twenty-four hours after surgical injury (1cm²) was applied LED therapy for 6 minutes during five days. Red LED presented best anti-edematous effects in comparison to the other wavebands. The perimeters were reduced in all groups, but in Green and Red LED groups were significantly diminished (p

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.325
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2015
Admission routes1
Has abstractyes

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