Rapid Computerized Assessment of Neurocognitive Deficits in Bipolar Disorder
Bibliographic record
Abstract
The purpose of this study is to illustrate the clinical usefulness of a computerized neuropsychological battery for identifying neurocognitive deficits in adults with bipolar disorder. Participants were 47 outpatients with bipolar disorder who were individually matched on age, education, sex, and ethnicity to 47 control subjects from the Central Nervous System (CNS) Vital Signs normative database. CNS Vital Signs is comprised of seven common neuropsychological measures, and it generates 15 primary scores that are used to calculate five domain scores (Memory, Psychomotor Speed, Reaction Time, Cognitive Flexibility, and Complex Attention). There was a significant multivariate effect and statistically significantly worse scores for those in the bipolar group on all five domain scores (medium to large effect sizes). When using two or more scores below the fifth percentile as a cutoff for neurocognitive impairment, 42.6% of the bipolar sample and only 6.4% of the control sample scored in this range. A subset of outpatients with bipolar disorder has frank neurocognitive impairments identifiable with this 30-40-minute computerized assessment battery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".