Clinical Factors That Predict Cognitive Function in Patients with Major Depression
Bibliographic record
Abstract
OBJECTIVES: To compare the performance of depressed patients to healthy control subjects on discrete cognitive domains derived from factor analysis and to examine the factors that may influence the performance of depressed patients on cognitive domains in a large sample. METHODS: We compared the cognitive performance of 149 patients with major depression to 104 healthy control subjects using multivariate ANCOVA. We used principal component factor analysis to group the cognitive variables into cognitive domains. Finally, we conducted regression analysis to examine the contribution of predictor factors to the cognitive domains that were impaired in the depressed group. RESULTS: Verbal memory and speed of processing were impaired in depressed patients, compared with healthy control subjects. Patient IQ, duration of depressive illness, and number of hospitalizations significantly contributed to the performance of patients on verbal memory and speed of processing. The severity of mood symptoms did not correlate with performance on any cognitive domain. CONCLUSIONS: Understanding the factors that predict cognitive performance of patients with depression may provide an insight into the processes by which depression leads to cognitive dysfunction. Our study showed that premorbid IQ and factors related to burden of illness are strong independent predictors of cognitive dysfunction in patients with major depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".