Bibliographic record
Abstract
OBJECTIVE: Over the last 15 years, researchers from around the world have developed instruments for assessing the risk of conversion to psychosis. The objective of this article is to review the literature on these instruments by focusing on genealogy links and on their performance in predicting conversion to psychosis. METHOD: A systematic review of articles published since 1980 relating to risk assessment instruments for conversion to psychosis by manual search and consultation of electronic databases MEDLINE, EMBASE, and PsycINFO. RESULTS: Three hundred ninety one (391) publications were selected and analyzed. Among these, 22 instruments were identified. These instruments are briefly described and placed on a timeline according to their year of publication. A code of positions, patterns, and forms is used to schematize the characteristics of each instrument. A table is presented to show changes in rates of conversion to psychosis within cohorts of subjects considered at risk according to the instruments. A second code of shades and outlines is used to schematize the characteristics of each cohort of patients. The two graphics set the stage for a discussion about the major strategies that were adopted to improve the performance of risk assessment instruments. CONCLUSION: These graphics allow a better understanding of the origin, evolution, current status, strengths, shortcomings, and future prospects of research on risk assessment instruments. Clinical ImplicationsThe integration of theoretical approaches, the multicenter studies, and the pre-selection of patients with short questionnaires were the main strategies to improve the performance of instruments assessing the risk of conversion to psychosis.These instruments are better at predicting conversion to psychosis than conventional variables within a more limited time span and can therefore enable the evaluation of various risk factors and biomarkers that may be associated with psychosis. LimitationsThe studies selected for this review of literature were not classified according to their methodological quality.These studies are based on heterogeneous populations and this must be taken into account when comparing the rates of conversion to psychosis.This review of literature was based on published data only and they were no direct communication with the authors of these instruments.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".