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Record W1540600021 · doi:10.1186/1471-2296-5-19

Is remission of depressive symptoms in primary care a realistic goal? A meta-analysis

2004· review· en· W1540600021 on OpenAlexafffund
Marliese Y. Dawson, Erin E. Michalak, Paul Waraich, John Anderson, Raymond W. Lam

Bibliographic record

VenueBMC Family Practice · 2004
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsBC Mental Health & Substance Use ServicesUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicinePsychological interventionDepression (economics)Primary careMeta-analysisPlaceboRandomized controlled trialMajor depressive disorderPillMEDLINEPsychiatryPhysical therapyInternal medicineAlternative medicineFamily medicineMood

Abstract

fetched live from OpenAlex

BACKGROUND: A primary goal of acute treatment for depression is clinical remission of symptoms. Most meta-analyses of remission rates involve randomized controlled trials (RCTs) using patients from psychiatric settings, but most depressed patients are treated in primary care. The goal of this study was to determine remission rates obtained in RCTs of treatment interventions for Major Depressive Disorder (MDD) conducted in primary care settings. METHODS: Potentially relevant studies were identified using computerized and manual search strategies up to May 2003. Criteria for inclusion included published RCTs with a clear definition of remission using established outcome measures. RESULTS: A total of 13 studies (N = 3202 patients) meeting inclusion criteria were identified. Overall remission rates for active interventions ranged between 50% and 67%, compared to 32% for pill placebo conditions and 35% for usual care conditions. CONCLUSIONS: Remission rates in primary care studies of depression are at least as high as for those in psychiatric settings. It is a realistic goal for family physicians to target remission of symptoms as an optimal outcome for 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)0.001
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: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.337
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.149
GPT teacher head0.409
Teacher spread0.260 · 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 designMeta-analysis
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

Citations30
Published2004
Admission routes2
Has abstractyes

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