What is normal cognition in depression? Prevalence and functional correlates of normative versus idiographic cognitive impairment.
Bibliographic record
Abstract
OBJECTIVE: Traditional neuropsychological assessment methods identify a subpopulation of individuals with Major Depressive Disorder (MDD) who demonstrate cognitive functioning below population norms. An even larger proportion of those with MDD self-report problems with cognition that interfere with daily roles and responsibilities. We aim to test whether an intraindividual deviation of cognitive functioning relative to premorbid estimates (idiographic impairment) may better characterize challenges for functional recovery in MDD. METHOD: Adult participants with MDD (N = 111) who completed a baseline neuropsychological assessment battery for a cognitive remediation trial were used in analyses. We compared the frequency of cognitive impairment using the normative and idiographic approaches and examined how these indexes related to observed functioning, perceived functioning, and depression severity. RESULTS: While only 25% of the sample would be classified as cognitively impaired on a composite measure according to normative comparison standards, 62.2% of this group were classified as idiographically impaired using a conservative cut-off of at least 1 SD deviation below premorbid estimates. Idiographic cognitive impairment shared a stronger inverse relationship with perceived functional competence than normative cognitive impairment. Depressive symptoms did not significantly correlate with both normative and idiographic impairment. CONCLUSIONS: In MDD, reliance on assessment of contemporary cognitive functioning might underestimate rates of those who could be considered cognitively impaired. Consideration of idiographic impairment may help explain gaps between normatively defined cognitive ability with subjective complaints and disability in MDD. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".