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Record W4392357639 · doi:10.1503/jpn.230137

Lessons from studies of medication reduction in psychosis: giving participants accurate information about risk in psychiatric research trials

2024· article· en· W4392357639 on OpenAlexvenueno aff
David Foreman

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2024
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsHarmRandomized controlled trialResearch ethicsPsychiatryMedicinePsychosisInformed consentPsychologyAlternative medicineSocial psychologySurgeryPathology

Abstract

fetched live from OpenAlex

All research needs ethical regulation, which is institutionalized in research ethics committees. The patient information sheet, approved by a research ethics committee, sets out what patients need to know to make an informed choice about research participation. However, guidance from research ethics committees is much less explicit about risk communication. In this commentary, the balance of risk in the patient information sheets from protocols of 2 randomized controlled trials (RCTs) of medication reduction in psychosis was compared with numbers needed to treat and harm from the literature. The patient information sheet omitted risk of excess death and incomplete recovery following relapse, and overestimated the anticipated benefits. All of these risks were demonstrated in the published results of 1 of the 2 RCTs. Quantifying and tabulating risk might improve patient information sheets.

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)0.559
metaresearch head score (Gemma)0.829
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.441
Threshold uncertainty score0.544

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5590.829
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0050.004
Science and technology studies0.0040.023
Scholarly communication0.0150.030
Open science0.0100.011
Research integrity0.0430.040
Insufficient payload (model declined to judge)0.0040.002

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 head0.224
GPT teacher head0.501
Teacher spread0.277 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations0
Published2024
Admission routes1
Has abstractyes

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