Perceived Sleep Quality Predicts Cognitive Function in Adults with Major Depressive Disorder Independent of Depression Severity
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to examine the role of perceived sleep quality in predicting subjective as well as objective cognitive function in adults with major depressive disorder (MDD). METHODS: Adults with recurrent MDD (n = 100) experiencing a major depressive episode of at least moderate severity and age-, sex-, and education-matched healthy controls (HC) (n = 100) were recruited to participate in a clinical trial validating the THINC-integrated tool (THINC-it; NCT02508493) for cognitive function. The THINC-it includes subjective and objective measures of cognitive function. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). RESULTS: < .001) and depression severity (P = .002) were found to independently predict impairments in subjective cognitive performance. Only perceived sleep quality predicted objective cognitive impairments (P = .017). Exploratory mediation analysis revealed depression severity to be a partial mediator of the relationship between perceived sleep quality and subjective cognitive performance (95% confidence interval [CI]: -0.56, -0.33). CONCLUSIONS: The results indicate that the subjective and objective cognitive impairments are differentially related to perceived sleep quality and depression severity and emphasize the importance of treating sleep disturbances in MDD.
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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) | 0.000 | 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 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".