MétaCan
Menu
Back to cohort
Record W7100854915

Guest Editorial Metabolic Effects of Antipsychotic Treatment: Between a Rock and a Hard Place?

2015· article· en· W7100854915 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Science and Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsNeglectLife expectancyAntipsychoticSchizophrenia (object-oriented programming)Mental illnessAffect (linguistics)DilemmaNexus (standard)Mental health
DOInot available

Abstract

fetched live from OpenAlex

t often feels as though medicine is practised “between a rock and a hard place. ” As physicians, we make difficult treatment decisions that can profoundly affect the lives of our patients. This is particularly true with the present generation of antipsychotic medications, where we have to deal with the paradox that some of our best medications are associated with the greatest metabolic side effects (1). With our patients and their families, we face the dilemma of seeing improvement in psychotic symptoms accompanied by significant weight gain, lipid disturbance, and occasionally, emergent diabetes. We appreciate that our primary goal is to treat psychiatric illness, but do we need to accept these side effects as inevitable and unavoidable? How do we understand and how do we manage metabolic risk when we treat psychosis? Are we truly keeping in mind the long-term interests of our patients? We have learned that patients with schizophrenia and other forms of severe mental illness have high rates of medical comorbidity (2) and that their life expectancy is shortened, primarily as a consequence of increased coronary heart dis-ease mortality (3). There are barriers to accessing medical care related both to psychiatric illness (for example, self-neglect and difficulty in communicating symptoms) and to the health care system (for example, lack of integrated mental and physi-cal health care). This has been aptly described as “duel neglect by patients and the system ” (4, p 1). Psychiatrists are physi-cians first. What is our responsibility here? How far should we extend our scope of practice? In this issue of The Canadian Journal of Psychiatry, 3 review papers offer an overview of our present state of knowledge in this area, with the ambitious goal of informing and shaping clinical practice. In the first paper, John Newcomer and Dan Haupt from the Washington University School of Medicine review the meta-bolic effects of antipsychotic treatment (5). John Newcomer is

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)0.003
metaresearch head score (Gemma)0.016
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0030.001
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0190.009

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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
GenreEditorial

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
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicComputer Science and EngineeringFrench-language works237,207