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Record W4233987288 · doi:10.1177/070674370505001319

Re: Hemorrhages during Escitalopram—Venlafaxine— Mirtazapine Combination Treatment of Depression

2005· article· en· W4233987288 on OpenAlexvenueno aff
Andrew Al‐Adwani

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2005
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsnot available
Fundersnot available
KeywordsMirtazapineVenlafaxineEscitalopramDepression (economics)MedicineReuptake inhibitorPsychiatryPsychologyAntidepressantAnxiety

Abstract

fetched live from OpenAlex

comorbidity modifies the outcome of a biological challenge.Actually, the dependent variables in our study were the affective symptoms of anxiety, depression, and aggression, whereas the Nardi and others study focuses on the physiologic variable of heart rate.The results of both studies taken together call for a reappraisal of the use of respiratory challenges in affective disorders in general.As well, the scope of subjects under study could be extended to mood disorder patients and anxiety disorder patients.Also, a more comprehensive scope of dependent variables should be assessed in future studies, encompassing several biological, physiological, and psychological dimensions of negative affectivity.

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)0.004
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: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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
GenreCommentary

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
Published2005
Admission routes1
Has abstractno

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