International consensus for the assessment of social cognition in neurocognitive disorders: framework definition and clinical recommendations of the SIGNATURE initiative
Bibliographic record
Abstract
BACKGROUND: Socio-cognitive assessment in neurocognitive disorders (NCDs) is rare in clinical practice and no consensus exists as to a uniform operationalization of socio-cognitive measures for NCDs in memory clinics. The SIGNATURE initiative aims to optimize the use of socio-cognitive measures in memory clinics, defining expert recommendations. We report consortium guidelines for the use of socio-cognitive measures in NCDs based on available evidence from the literature and the current state of practices in memory clinics. METHODS: Using a Delphi consensus method supported by a literature review and the results of an international survey, 22 specialists defined recommendations for the context of use, relevance in NCD diagnosis, priorities for future research and facilitators/obstacles of socio-cognitive assessment in major and mild NCDs. RESULTS: Overall, panelists recommended social cognition testing in routine diagnostic assessment to evaluate both socio-cognitive and socio-behavioral alterations. A set of clinical, methodological, implementation and external factors facilitating or hampering the use of socio-cognitive tasks was identified. CONCLUSIONS: This is the first focused endeavor to favor the implementation of socio-cognitive assessment, which is required by DSM-5 but seldom performed despite clear evidence of its clinical relevance for diagnosis and care. Our results provide an initial set of recommendations, refinable through the future actions of the SIGNATURE initiative. Future collaborative clinical research projects should overcome current limitations and foster the use of ecological and cross-culturally validated measures in clinics.
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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 | 0.205 | 0.186 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.015 | 0.016 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".