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Record W1979881132 · doi:10.1080/07420520500263292

Therapeutic Mechanism in Seasonal Affective Disorder: Do Fluoxetine and Light Operate Through Advancing Circadian Phase?

2005· article· en· W1979881132 on OpenAlexaff
Greg Murray, Erin E. Michalak, Anthony Levitt, Robert D. Levitan, Murray W. Enns, Rachel Morehouse, Raymond W. Lam

Bibliographic record

VenueChronobiology International · 2005
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsSaint John Regional HospitalDalhousie UniversityUniversity of ManitobaUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsCircadian rhythmFluoxetineLight therapyPlaceboContext (archaeology)PsychologyChronotypeDark therapyMechanism (biology)AntidepressantDepression (economics)Internal medicineMedicinePsychiatryNeuroscienceBiologySerotoninAnxiety

Abstract

fetched live from OpenAlex

In the context of Lewy's phase delay hypothesis, the present study tested whether effective treatment of winter Seasonal Affective Disorder (SAD) is mediated by advancing of circadian phase. Following a baseline week, 78 outpatients with SAD were randomized into 8 weeks of treatment with either fluoxetine and placebo light treatment or light treatment and placebo pill. Depression levels were measured on the Ham17+7 and the BDI-II, and circadian phase was estimated on the basis of daily sleep logs and self-reported morningness-eveningness. Among the 61 outpatients with complete data, both treatments were associated with significant antidepressant effect and phase advance. However, pre- and post-treatment comparisons found that the degree of symptom change did not correlate with the degree of phase change associated with treatment. The study therefore provides no evidence that circadian phase advance mediates the therapeutic mechanism in patients with SAD. Findings are discussed in terms of the limitations of the circadian measures employed.

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 score0.302
Threshold uncertainty score0.736

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)0.0010.000

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 head0.012
GPT teacher head0.286
Teacher spread0.274 · 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

Citations45
Published2005
Admission routes1
Has abstractyes

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