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Record W2810096032 · doi:10.1186/s12916-018-1096-5

Optimising first- and second-line treatment strategies for untreated major depressive disorder — the SUN☺D study: a pragmatic, multi-centre, assessor-blinded randomised controlled trial

2018· article· en· W2810096032 on OpenAlexaff
Tadashi Kato, Toshi A. Furukawa, Akio Mantani, Kenichi Kurata, Hajime Kubouchi, Susumu Hirota, Hirotoshi Sato, Kazuyuki Sugishita, Bun Chino, Kahori Itoh, Yoshio Ikeda, Yoshihiro Shinagawa, Masaki Kondo, Yasumasa Okamoto, Hirokazu Fujita, Motomu Suga, Shingo Yasumoto, Naohisa Tsujino, Takeshi Inoue, Noboru Fujise, Tatsuo Akechi, Mitsuhiko Yamada, Shinji Shimodera, Norio Watanabe, Masatoshi Inagaki, Kazuhira Miki, Yusuke Ogawa, Nozomi Takeshima, Yu Hayasaka, Aran Tajika, Kiyomi Shinohara, Naohiro Yonemoto, Shiro Tanaka, Qi Zhou, Gordon Guyatt

Bibliographic record

VenueBMC Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsMcMaster University
FundersNovartis PharmaAstellas PharmaShionogiEisaiPfizerDaiichi-SankyoGlaxoSmithKlineAstraZenecaMinistry of Health, Labour and WelfareEli Lilly and Company
KeywordsMedicineSertralineMirtazapineDepression (economics)Confidence intervalMajor depressive disorderPatient Health QuestionnaireAntidepressantRandomized controlled trialGuidelineMajor depressive episodeDepressive symptomsInternal medicinePsychiatryPhysical therapyMoodCognition

Abstract

fetched live from OpenAlex

BACKGROUND: For patients starting treatment for depression, current guidelines recommend titrating the antidepressant dosage to the maximum of the licenced range if tolerated. When patients do not achieve remission within several weeks, recommendations include adding or switching to another antidepressant. However, the relative merits of these guideline strategies remain unestablished. METHODS: This multi-centre, open-label, assessor-blinded, pragmatic trial involved two steps. Step 1 used open-cluster randomisation, allocating clinics into those titrating sertraline up to 50 mg/day or 100 mg/day by week 3. Step 2 used central randomisation to allocate patients who did not remit after 3 weeks of treatment to continue sertraline, to add mirtazapine or to switch to mirtazapine. The primary outcome was depression severity measured with the Patient Health Questionnaire-9 (PHQ-9) (scores between 0 and 27; higher scores, greater depression) at week 9. We applied mixed-model repeated-measures analysis adjusted for key baseline covariates. RESULTS: Between December 2010 and March 2015, we recruited 2011 participants with hitherto untreated major depression at 48 clinics in Japan. In step 1, 970 participants were allocated to the 50 mg/day and 1041 to the 100 mg/day arms; 1927 (95.8%) provided primary outcomes. There was no statistically significant difference in the adjusted PHQ-9 score at week 9 between the 50 mg/day arm and the 100 mg/day arm (0.25 point, 95% confidence interval (CI), - 0.58 to 1.07, P = 0.55). Other outcomes proved similar in the two groups. In step 2, 1646 participants not remitted by week 3 were randomised to continue sertraline (n = 551), to add mirtazapine (n = 537) or to switch to mirtazapine (n = 558): 1613 (98.0%) provided primary outcomes. At week 9, adding mirtazapine achieved a reduction in PHQ-9 scores of 0.99 point (0.43 to 1.55, P = 0.0012); switching achieved a reduction of 1.01 points (0.46 to 1.56, P = 0.0012), both relative to continuing sertraline. Combination increased the percentage of remission by 12.4% (6.1 to 19.0%) and switching by 8.4% (2.5 to 14.8%). There were no differences in adverse effects. CONCLUSIONS: In patients with new onset depression, we found no advantage of titrating sertraline to 100 mg vs 50 mg. Patients unremitted by week 3 gained a small benefit in reduction of depressive symptoms at week 9 by switching sertraline to mirtazapine or by adding mirtazapine. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01109693 . Registered on 23 April 2010.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.057
GPT teacher head0.356
Teacher spread0.299 · 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 designRandomized trial
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

Citations60
Published2018
Admission routes1
Has abstractyes

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