Harmonizing the structured interview for psychosis‐risk syndromes (<scp>SIPS</scp>) and the comprehensive assessment of at‐risk mental states (<scp>CAARMS</scp>): An initial approach
Bibliographic record
Abstract
The two most used semi-structured psychometric instruments that define criteria for being at clinical high risk (CHR) for psychosis are the Comprehensive Assessment of At-Risk Mental States (CAARMS) and the Structured Interview for Psychosis-Risk Syndromes (SIPS). Although very similar there are important differences between these two measures. Developing harmonized psychometric criteria for defining CHR and associated outcomes would be beneficial for future research. This article describes the first step in this process by reporting on a NIMH workshop held in Washington DC, in February 2019 that was attended by experts in the field. The aim of this workshop was to examine the similarities and differences between the two measures and consider how the harmonization process could proceed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".