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Record W2020403060 · doi:10.1117/12.647201

Transmission of phototherapy through human skin: dosimetry adjustment for effects of skin color, body composition, wavelength, and light coupling to skin

2006· article· en· W2020403060 on OpenAlexafffund
Ethne L. Nussbaum, J. van Zuylen

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsUniversity of TorontoMount Sinai Hospital
FundersPhysiotherapy Foundation of Canada
KeywordsMaterials scienceOpticsWavelengthIrradiationDiodeOptoelectronicsLight-emitting diodePenetration (warfare)LaserInfraredPhysics

Abstract

fetched live from OpenAlex

Purpose: To examine factors that affect penetration of phototherapy. Methods: Age, sex, height, and weight were recorded; skin color, skinfold thickness, and light transmission through a skinfold were measured over biceps and triceps muscles, and at the anterior waistline. Light was generated using two 23-diode LED arrays at 840 nm and 660 nm with surface area of 7 cm<sup>2</sup>. Photon irradiation was measured using an Optical Power Meter consisting of a 1x1-cm<sup>2</sup> light detector placed in the centre of the illuminated 7 cm<sup>2</sup> spot. Transmission was measured using three skin-diode coupling conditions. Results: Penetration of LED irradiation increased when diodes were coupled to skin with pressure. Red light attenuated more rapidly than infrared light and the attenuation of red light increased as skin color darkened. Penetration of red and infrared light decreased as the amount of subcutaneous fat increased. There were gender effects on penetration of infrared light at normal and low BMI values. Conclusions: When using divergent light sources for phototherapy, radiant exposure should take into account individual physical characteristics, irradiation wavelength and diode configuration of the laser therapy system.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.007
GPT teacher head0.245
Teacher spread0.238 · 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 designBench or experimental
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

Citations1
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSkin Protection and AgingFrench-language works237,207