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Record W2128627316 · doi:10.1192/apt.bp.105.001735

Herbal remedies for depression and anxiety

2007· article· en· W2128627316 on OpenAlexaff
Edzard Ernst

Bibliographic record

VenueAdvances in Psychiatric Treatment · 2007
Typearticle
Languageen
FieldMedicine
TopicMedicinal Plant Extracts Effects
Canadian institutionsVictoria Park
FundersUniversität WienUniversity of Exeter
KeywordsHypericum perforatumKavaTraditional medicineMedicineMelissa officinalisAnxietyDepression (economics)Salvia officinalisPsychiatryOfficinalis

Abstract

fetched live from OpenAlex

Herbal remedies are used by many people suffering from anxiety or depression. It is therefore important to know whether they generate more good than harm. A systematic review of the published literature revealed trial data for Ginkgo biloba, Lavandula angustifolia, Hypericum perforatum, Valeriana officinalis, Crataegus oxyacantha, Eschscholzia californica, Matricaria recutita, Melissa officinalis, Passiflora incarnate and Piper methysticum. Only two of these herbal remedies are supported by sound evidence: Hypericum perforatum (St John's wort) for mild to moderate depression and Piper methysticum (kava) for anxiety. Neither is free of risks. Our knowledge of herbal remedies is incomplete and the subject merits rigorous study.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.009
GPT teacher head0.327
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations34
Published2007
Admission routes1
Has abstractyes

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