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Record W1589662182 · doi:10.1139/jpn.0306

Mirtazapine for treatment-resistant depression: a preliminary report

2003· article· en· W1589662182 on OpenAlexaffvenue
Dante D. C. Wan, Divya Kundhur, Kevin Solomons, Lakshmi N. Yatham, Raymond W. Lam

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2003
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversity of British Columbia Hospital
Fundersnot available
KeywordsMirtazapineTolerabilityAntidepressantNauseaMajor depressive disorderTreatment-resistant depressionDepression (economics)MedicineMajor depressive episodePsychologyPsychiatryInternal medicineAdverse effectMood

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the effectiveness and tolerability of mirtazapine, a noradrenergic and specific serotonergic antidepressant, in the open-label treatment of patients with depression who were resistant to other antidepressant agents. METHODS: The charts of 24 patients who met the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, (DSM-IV) criteria for major depressive disorder and were treated with mirtazapine after partial or nonresponse to standard antidepressants were reviewed for clinical response. Outcome was determined by using the Clinical Global Impressions of Improvement (CGI-I) Scale. RESULTS: Symptomatic improvement was observed in 9 (38%) of 24 patients during an average of 14.1 months of mirtazapine treatment at a mean dose of 36.7 mg/day. Five (21%) patients discontinued mirtazapine because of side effects such as fatigue, weight gain and nausea. Five (21%) patients were receiving combination therapy with another antidepressant when mirtazapine treatment was initiated. CONCLUSIONS: This open-label study suggests that a subgroup of patients with treatment-resistant depression may benefit from mirtazapine treatment. Further controlled studies are required to demonstrate the efficacy of mirtazapine in treatment-resistant depression.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.338

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 head0.027
GPT teacher head0.315
Teacher spread0.288 · 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 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

Citations21
Published2003
Admission routes2
Has abstractyes

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