Bibliographic record
Abstract
Aims: Cognitive heterogeneity in schizophrenia spectrum disorders (SSD) and bipolar disorder (BD) has been explored using clustering analyses.However, the resulting subgroups have not been cognitively validated beyond measures used as clustering variables themselves.We compared the emergent cross-diagnostic subgroups of SSD and BD patients on measures used to classify them, and also across a range of alternative cognitive measures assessing some of the same constructs. Methods: Domain scores from the Matrics Consensus CognitiveBattery were used in a cross-diagnostic clustering analysis of 86 patients with SSD (n = 45) and BD (n = 41).The emergent subgroups were then compared to each other and healthy controls (n = 76) on these and alternative measures of these domains, as well as on premorbid IQ, global cognition and a proxy of cognitive decline.Results: A three-cluster solution was most appropriate, with subgroups labelled as Globally Impaired, Selectively Impaired, and Superior/Near-Normal relative to controls.With the exception of processing speed performance, the subgroups were generally differentiated on the cognitive domain scores used as clustering variables.Differences in cognitive performance among these subgroups were not always statistically significant when compared on the alternative cognitive measures.There was evidence of global cognitive impairment and putative cognitive decline in the two cognitively impaired subgroups.Limitations: For clustering analysis, sample size was relatively small.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.661 | 0.464 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".