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Record W2883460108 · doi:10.4088/jcp.17m11856

Appetitive Symptoms Differentially Predict Treatment Response to Fluoxetine, Light, and Placebo in Nonseasonal Major Depression

2018· article· en· W2883460108 on OpenAlexafffund
Robert D. Levitan, Anthony Levitt, Erin E. Michalak, Rachel Morehouse, Rajamannar Ramasubbu, Lakshmi N. Yatham, Edwin M. Tam, Raymond W. Lam

Bibliographic record

VenueThe Journal of Clinical Psychiatry · 2018
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversity of CalgarySaint John Regional HospitalUniversity of TorontoDalhousie UniversityCentre for Movement DisordersSunnybrook Health Science CentreCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsFluoxetinePlaceboMajor depressive disorderLight therapyPsychologyDepression (economics)Rating scaleHamilton Rating Scale for DepressionInternal medicineMorningPsychiatryMedicineCognitionDevelopmental psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: We previously reported that morning bright light therapy is efficacious in adults with nonseasonal major depressive disorder (MDD), both on its own and in combination with fluoxetine. Given that appetitive symptoms predict response to bright light therapy in seasonal depression, we examined, in this secondary analysis, whether the same held true in these nonseasonal MDD patients. METHODS: Data were collected from October 7, 2009, to March 11, 2014. One hundred twenty-two patients who met DSM-IV-TR criteria for MDD without a seasonal pattern were randomly assigned to light monotherapy, fluoxetine, combination light and fluoxetine, or double-placebo (inactivated negative ion generator plus placebo pill). Multiple regression assessed the percentage change in Montgomery-Asberg Depression Rating Scale (MADRS) scores based on treatment condition, appetitive symptom score at baseline (sum of 4 items on the Structured Interview Guide for the Hamilton Depression Rating Scale, Seasonal Affective Disorders version), and the condition-by-appetitive score interaction. Sex was considered as a possible moderator of these effects. RESULTS: The overall regression model predicting treatment response was highly significant (P < .001), and the treatment condition-by-appetitive score interaction was a strong predictor of MADRS change scores (t = 2.65, P = .009). For individuals in the placebo group, more appetitive symptoms at baseline predicted less decrease in MADRS scores at 8 weeks (r = -0.37; large effect size). In contrast, for individuals in the active treatment groups, more appetitive symptoms at baseline predicted more of a decrease in depression scores at 8 weeks (fluoxetine group r = +0.23, medium effect size; light therapy group r = +0.11, small effect size; combination group r = +0.32, medium to large effect size). No moderation effect of sex was found. CONCLUSIONS: More severe appetitive symptoms at baseline predicted treatment response differentially across the 4 treatment groups. Contrary to prior findings in seasonal depression, this association was not robust for MDD patients receiving light therapy alone, although it was stronger in patients receiving fluoxetine with or without light. As the group sample sizes were modest, the current findings should be considered as preliminary only. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00958204.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.026
GPT teacher head0.346
Teacher spread0.320 · 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 designObservational
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

Citations7
Published2018
Admission routes2
Has abstractyes

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