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Record W2112023200 · doi:10.1192/bjp.180.1.45

Signs and Symptoms of Psychotic Illness (SSPI): A rating scale

2002· article· en· W2112023200 on OpenAlexaff
Peter F. Liddle, Elton T.C. Ngan, Gary Duffield, King H. Kho, Anthony J. Warren

Bibliographic record

VenueThe British Journal of Psychiatry · 2002
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIntraclass correlationRating scaleInter-rater reliabilityPsychologyClinical Global ImpressionPsychiatryAntipsychoticPsychometricsClinical psychologyMedicineSchizophrenia (object-oriented programming)Developmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: In the rating scales commonly used for assessing response to antipsychotic treatment, individual items embrace symptoms that apparently arise from distinguishable pathophysiological processes and might be expected to respond differently to treatment. AIMS: To test the reliability, sensitivity to change and factor structure of a new scale for the assessment of the Signs and Symptoms of Psychotic Illness (the SSPI). METHOD: Interrater reliability was evaluated by determining the intraclass correlation for the ratings of 63 patients. Sensitivity to change was assessed in a longitudinal study of 33 patients. Factor structure was determined from scores for 155 patients. RESULTS: The intraclass correlation was satisfactory for all individual items and excellent for the total score. Scores were sensitive to change. A change in Clinical Global Impression of one unit corresponded to an SSPI total score change of 31%. Factor analysis revealed five clusters of symptoms. CONCLUSIONS: The SSPI provides a sensitive and reliable measure of the five major clusters of symptoms that occur commonly in psychotic illness.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: Other design
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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 designOther design
Domainnot available
GenreMethods

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

Citations141
Published2002
Admission routes1
Has abstractyes

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