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When Less Is More: Reducing the Incidence of Antipsychotic Polypharmacy

2007· review· en· W1981889302 on OpenAlexaff
William Tucker

Bibliographic record

VenueJournal of Psychiatric Practice · 2007
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsColumbia College
Fundersnot available
KeywordsPolypharmacyAntipsychoticAutonomyMental healthMedicinePsychiatrySchizophrenia (object-oriented programming)Intensive care medicinePolitical science

Abstract

fetched live from OpenAlex

In 2003, the New York State Office of Mental Health initiated a program aimed at supporting patient recovery by simplifying antipsychotic regimens. A key component of the program, which has been essential in supporting physician autonomy, was the introduction of a software program, Psychiatric Clinical Knowledge Enhancement System, termed "PSYCKES." This software program enables physicians to visualize at a glance the medication history of each of their patients as well as of their colleagues' patients, as a way of making better-informed decisions. The fiscal impact, in the direction of a significant reduction in antipsychotic polypharmacy, was not lost on policy-makers, who have included $1.3 million in the current state budget for the dissemination of this program.

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)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.105
GPT teacher head0.470
Teacher spread0.365 · 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

Citations5
Published2007
Admission routes1
Has abstractyes

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