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Record W2942888071 · doi:10.5539/jas.v11n6p138

Use of Thermography in Reconstructive Surgery Associated With Laser Therapy—Review of Literature

2019· article· en· W2942888071 on OpenAlexvenueno aff
Stephanie Szpoganicz Gambardella, Josiane Morais Pazzini, Julielton de Souza Barata, Bruna Fernanda Firmo, Jorge Luis Álvarez Gómez, Andrígo Barboza De Nardi

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVitalityThermographyHuman medicineReconstructive surgeryOrthopedic surgeryLow level laser therapyLaser therapySurgeryLaserTraditional medicineBiology

Abstract

fetched live from OpenAlex

Based on temperature charts, infrared skin thermography is widely used in human medicine, but little known in veterinary medicine. The application of the technique allows orthopedic clinical follow-up as well as aids in the diagnosis of breast tumors in humans, due to its ability to correlate vasculature alterations and tissue vitality with modification of the temperature pattern. For this reason it is applied in veterinary medicine for the detection of joint injuries in horses and animal production, little covering the medicine of small animals. Against of these phages the present study aims to elucidate and suggest its use for the diagnosis and postoperative follow-up of reconstructive surgeries in animals, as well as to evaluate therapeutic measures that seek to improve cutaneous healing, such as low power laser therapy.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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 designNot applicable
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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