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Record W2323974827 · doi:10.1055/s-0034-1387784

Timing of Adjunctive Therapy in the Treatment of Depression: A Chart Review

2014· review· en· W2323974827 on OpenAlexaff
Ryosuke Tarumi, Takefumi Suzuki, Hideaki Tani, R. Den, Norifusa Sawada, Hitoshi Sakurai, C. Tsutsumi-Ozawa, Ai Ohtani, Masaru Mimura, Hiroyuki Uchida

Bibliographic record

VenuePharmacopsychiatry · 2014
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsMedicineDepression (economics)AntidepressantMedical prescriptionAdjunctive treatmentPsychiatryMajor depressive disorderReferralMajor depressive episodeAntidepressant medicationPediatricsInternal medicineMoodFamily medicineAnxiety

Abstract

fetched live from OpenAlex

INTRODUCTION: The objective of this study was to examine the evolution of antidepressant switch and adjunctive therapy. METHODS: This chart review was conducted at 6 primary psychiatric clinics or hospitals, in Tokyo, Japan. A chart review of longitudinal prescriptions was conducted regarding 633 outpatients with major depressive disorder for up to 2 years after their first visit. Patients who had already received antidepressants prior to the visit were excluded. RESULTS: 22.6% (N=143) of the patients completed or continued the outpatient treatment over the 2 years while 27 (4.3%), 23 (3.6%), and 439 (69.4%) patients discontinued it due to hospitalization, referral to another clinic, and loss to follow-up, respectively. A total of 597 episodes of antidepressant treatment were identified. Among them, 482 episodes (80.7%) were associated with the suggested dose ranges while antidepressant drugs were under-dosed in 19.3% (N=115) of the episodes. 50 patients (7.9%) received adjunctive therapy; it was employed after a median of only one antidepressant had been tried. CONCLUSION: Psychiatrists may be hasty in prescribing an adjunctive therapy in the treatment of 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
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)0.0000.000

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 head0.107
GPT teacher head0.444
Teacher spread0.337 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations0
Published2014
Admission routes1
Has abstractyes

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