MétaCan
Menu
Back to cohort
Record W2069099662 · doi:10.3138/ptc.59.3.194

Transmission of Light through Human Skin Folds during Phototherapy: Effects of Physical Characteristics, Irradiation Wavelength, and Skin-Diode Coupling

2007· article· en· W2069099662 on OpenAlexfundvenueno aff
Ethne L. Nussbaum, J. van Zuylen, Jing Fang

Bibliographic record

VenuePhysiotherapy Canada · 2007
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsnot available
FundersHealth Canada
KeywordsMaterials scienceLight-emitting diodeDiodeOptoelectronicsTransmission (telecommunications)OpticsWavelengthInfraredLaserMedicineBiomedical engineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Purpose: There are no available guidelines for adjusting radiant exposure of phototherapy to accommodate for certain patient characteristics, as well as for the light source being used. We examined factors that affect light transmission using light-emitting diodes (LEDs). Methods: Age, gender, height, and weight were recorded. Skin colour, skin-fold thickness, and light transmission through a skin fold were measured over biceps and triceps muscles and at the waistline. Light was generated using LED arrays at 840 and 660 nm. Transmission was measured using three skin-diode coupling conditions. Results: Transmission was greatest when diodes were coupled directly to skin with pressure, with transmission of infrared light 2.5 times that of red light. As body mass index (BMI) increased, transmission decreased. Transmission of red light decreased with increasing skin darkness. Skin colour did not affect transmission of infrared light. There was a gender difference for infrared light transmission in individuals with a low BMI. Conclusions: Radiant exposure of LED phototherapy should be adjusted based on patient characteristics, as well as on laser system characteristics, to ensure that the needed fluence reaches the target tissue.

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 score0.130
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.004
GPT teacher head0.271
Teacher spread0.267 · 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

Citations30
Published2007
Admission routes2
Has abstractyes

Explore more

Same venuePhysiotherapy CanadaSame topicLaser Applications in Dentistry and MedicineFrench-language works237,207