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Record W1935920152 · doi:10.1017/s1092852900028261

Initial Effectiveness, Partial Remission, and Full Remission in Depression: Focus on Long-Term Treatment with SNRIs

2008· article· en· W1935920152 on OpenAlexaff
Serdar Dursun

Bibliographic record

VenueCNS Spectrums · 2008
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMilnacipranAntidepressantDepression (economics)PlaceboMedicineInternal medicineDepressive symptomsPsychiatryPsychologyCognitionAnxiety

Abstract

fetched live from OpenAlex

Full remission, defined as the absence of all significant symptoms of depression over at least 6 months, is the ultimate goal of antidepressant therapy. Remission takes time and studies have shown that remission rates continue to rise for at least 3 months after initial improvement. Depression is a recurrent condition with a cumulative probability of recurrence of 40% over 2 years and 70% over 5 years after the first depressive episode. In addition the risk of recurrence increases with each new depressive episode. Continuing antidepressant treatment beyond the acute response significantly decreases the risk of recurrence. A double-blind study with the serotonin norepinephrine reuptake inhibitor milnacipran, for example, has shown that patients in remission following treatment with milnacipran who continued the active treatment for a further 12 months had significantly less relapse (P<.05) than those switched to placebo. In spite of the importance of maintaining antidepressant therapy, many patients do not continue treatment. Among the principal reasons for this are side effects and worries of psychological or physical dependence. To reduce the risk of relapse, treatment with effective, well-tolerated antidepressants with few withdrawal effects should be pursued for at least 6 months and possibly longer in patients already experiencing relapse.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.022
GPT teacher head0.290
Teacher spread0.268 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

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