Evidence supporting the use of a brief cognitive assessment in routine clinical assessment for psychosis
Bibliographic record
Abstract
Cognitive impairment is a core feature of psychosis. Full cognitive assessments are not often conducted in routine clinical practice as administration is time-consuming. Here, we investigated whether brief tests of cognition could be used to predict broader neurocognitive performance in a manner practical for screening use in mental health services. We carried out a principal component analysis (PCA) to obtain an estimate of general cognitive function (N = 415). We investigated whether brief tests of memory accounted for a significant percentage of variation in the PCA scores. We used discriminant function analysis to determine if measures could predict classification as lower, intermediate or higher level of cognitive function and to what extent these groups overlapped with groups based on normative data. Memory tests correctly classified 65% of cases in the highest scoring group, 35% of cases in the intermediate group, and 77% of cases in the lowest scoring group. These PCA-derived groups and groups based on normative scores for the two tests were significantly associated (χ2 = 164.00, p < 0.001). These measures accurately identified three quarters of the low performing group, the group of greatest interest from the perspective of identifying those likely to need greater supports as part of clinical care. In so doing they suggest a potentially useful approach to screening for cognitive impairment in clinical services, upon which further assessment can be built if required.
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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 | 0.026 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".