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Record W2419432233

Treatment costs of venlafaxine and selective serotonin-reuptake inhibitors for depression and anxiety.

2002· article· en· W2419432233 on OpenAlexaff
George J. Wan, William H. Crown, Ernst R. Berndt, Stan N. Finkelstein, Davina C. Ling

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsVenlafaxineDepression (economics)MedicineAnxietyReuptake inhibitorVenlafaxine HydrochlorideSerotonin reuptake inhibitorSerotonin Uptake InhibitorsPsychiatryInternal medicineSerotoninAntidepressantFluoxetine
DOInot available

Abstract

fetched live from OpenAlex

In this article, health care expenditures are assessed for patients diagnosed with depression who are being treated with either venlafaxine (immediate or extended release) or a selective serotonin-reuptake inhibitor (SSRI). Patients beginning treatment for a new depressive episode were identified retrospectively from 1994 to 1998. Before beginning therapy, patients prescribed venlafaxine (N = 353) had more nonmental illnesses (0.84 vs. 0.75 clinical events/patient, respectively; P < .01) and hospitalizations for mental illness (0.56 vs. 0.30 hospitalizations/patient; P = .06) than patients prescribed SSRIs (N = 7,330). In the six months after initiating treatment, venlafaxine was associated with lower hospitalization expenditures for nonmental illness than were SSRIs ($206 vs. $472, respectively; P = .02), but total health care expenditures were not significantly different.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.240
Teacher spread0.218 · 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 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

Citations7
Published2002
Admission routes1
Has abstractyes

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