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Blue blocker glasses impede the capacity of bright light to suppress melatonin production

2006· article· en· W2036522562 on OpenAlexafffund
Alexandre Sasseville, Nathalie Paquet, Jean Sévigny, Marc Hébert

Bibliographic record

VenueJournal of Pineal Research · 2006
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsMelatoninBlue lightIlluminanceOpticsLens (geology)Biological clockCircadian rhythmOrange (colour)MorningOphthalmologyEndocrinologyMedicineInternal medicinePhysics

Abstract

fetched live from OpenAlex

Blocking morning light exposure with dark goggles can contribute to the adjustment to night work but these glasses are incompatible with driving. Recently, it was discovered that the biological clock is most sensitive to short wavelengths (blue light). Therefore, we tested the hypothesis that cutting the blue portion of the light spectrum with orange lens glasses (blue blockers) would prevent the light-induced melatonin suppression, a test broadly used as an indirect assessment of the circadian clock sensitivity. Fourteen normal subjects were exposed at night to a 60 min bright light pulse (1300 lx behind filters) between 01:00 and 02:00 hr while wearing orange lens glasses (experimental condition) or grey lens glasses (control condition). The amount of salivary melatonin change observed during the light pulse was compared with a melatonin baseline obtained the night before. Although both glasses transmitted the same illuminance (1300 lx) but at an irradiance 25% higher for the orange lens (408 microW/cm2) compared with the grey lens (327 microW/cm2), a non-significant increase of 6% (95% CI, -20% to 9%) was observed with the orange lens whereas a significant (P < 0.05) reduction of 46% (95% CI, 35-57%) was observed with the grey lens. Blue blockers represent an elegant means to prevent the light-induced melatonin suppression. Further studies are needed to show that these glasses, which are suitable for driving, could facilitate adaptation to night work.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.046
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.346
Teacher spread0.276 · 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

Citations141
Published2006
Admission routes2
Has abstractyes

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