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Record W4242459086 · doi:10.1017/s1092852900003023

CNS volume 13 supplement 9 Cover and Front matter

2008· article· en· W4242459086 on OpenAlexaff
Andrew J. Cutler, Sara Ball, Stephen M. Stahl

Bibliographic record

VenueCNS Spectrums · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsAbbott (Canada)
FundersNational Eye InstituteVanda Pharmaceuticals
KeywordsCover (algebra)Front coverVolume (thermodynamics)Front (military)Action (physics)Content (measure theory)Computer sciencePhysicsEngineeringMathematics

Abstract

fetched live from OpenAlex

Atypical antipsychotic treatment strategies have tended to change significantly from the time new drugs are introduced to the market.Because these drugs are associated with so many side effects, dosing strategies are largely based on the avoidance of adverse effects.A great number of issues complicate the task of effectively dosing atypical antipsychotics, including great variability and unpredictability in individual response.This often leads to extensive periods of trial and error as well as patient suffering, while physicians search for an optimum treatment.The mechanisms of action of these drugs are not entirely understood; they can be effective for both psychosis and mood disorders, possibly via actions on different systems or circuits.Furthermore, a resolution to the dilemma of dosing once or twice daily depends on issues of patient compliance and drug efficacy.This supplement puts these issues into perspective by illustrating historical discrepancies in antipsychotic use between clinical trials and practice, discussing individual response variability to antipsychotic treatment, describing current theories of antipsychotic mechanisms of action, and speculating as to the future of antipsychotic treatment strategies.The following unmet needs regarding dosing atypical antipsychotics were revealed following a vigorous assessment of activity feedback, expert faculty assessment, literature review, and through new medical knowledge: (1) although many clinicians generalize that higher doses are associated with greater efficacy and more side effects, the efficacy and tolerability profile for different doses of atypical antipsychotics are far less straightforward than that; (2) physicians continue to face huge issues of patient adherence when treating schizophrenia and bipolar disorder, which may be mitigated by addressing the most troublesome side effects caused by antipsychotics;(3) research on genetics, neurocircuitry, and new treatment methods in schizophrenia is ongoing; clinicians need to be educated on new treatment strategies as data accumulate so that they are prepared to implement these tools once they become available.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.611
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.6110.380

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 head0.026
GPT teacher head0.187
Teacher spread0.161 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2008
Admission routes1
Has abstractyes

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