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Record W3107513910 · doi:10.1016/j.schres.2020.11.040

Consistency checks to improve measurement with the Personal and Social Performance Scale (PSP)

2020· article· en· W3107513910 on OpenAlexaff
Jonathan Rabinowitz, Mark Opler, Alon A. Rabinowitz, Selam Negash, Ariana Anderson, Dong Jing Fu, David J. Williamson, Alan Kott, Lori L. Davis, Nina R. Schooler

Bibliographic record

VenueSchizophrenia Research · 2020
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsAmerican Water (Canada)
FundersSeventh Framework Programme
KeywordsFLAGS registerDecilePsychologyFlag (linear algebra)Consistency (knowledge bases)Reliability (semiconductor)Scale (ratio)Rating scaleClinical psychologyPsychiatryStatisticsComputer scienceMathematicsDevelopmental psychologyGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

International Society for CNS Clinical Trials and Methodology convened an expert Working Group that assembled consistency/inconsistency flags for the Personal and Social Performance Scale (PSP). One hundred and forty seven flags were identified, 16 flag errors in deriving the PSP decile (i.e., total) score from the four individual domain scores, 74 flag inconsistencies between domain scores relative to Positive and Negative Symptom Scale (PANSS) item ratings and 57 flag inconsistencies between PSP decile score and PANSS items ratings. The flags were applied to assessments from randomized clinical trial data of antipsychotics in schizophrenia from almost 18,000 ratings. Twenty-two flags were raised in at least 5 of 1000 ratings. Nearly 20% of the PSP ratings had at least one inconsistency flag raised. Application of flags to clinical ratings may improve the reliability of ratings and validity of trials.

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)0.497
metaresearch head score (Gemma)0.535
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.503
Threshold uncertainty score0.621

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4970.535
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0090.010
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.004
Research integrity0.0020.003
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 head0.082
GPT teacher head0.321
Teacher spread0.239 · 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 designObservational
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

Citations10
Published2020
Admission routes1
Has abstractyes

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