The magnitude of neurocognitive impairment is overestimated in depression: the role of motivation, debilitating momentary influences, and the overreliance on mean differences
Bibliographic record
Abstract
BACKGROUND: Meta-analyses agree that depression is characterized by neurocognitive dysfunctions relative to nonclinical controls. These deficits allegedly stem from impairments in functionally corresponding brain areas. Increasingly, studies suggest that some performance deficits are in part caused by negative task-taking attitudes such as poor motivation or the presence of distracting symptoms. A pilot study confirmed that these factors mediate neurocognitive deficits in depression. The validity of these results is however questionable given they were based solely on self-report measures. The present study addresses this caveat by having examiners assess influences during a neurocognitive examination, which were concurrently tested for their predictive value on performance. METHODS: Thirty-three patients with depression and 36 healthy controls were assessed on a battery of neurocognitive tests. The examiner completed the Impact on Performance Scale, a questionnaire evaluating mediating influences that may impact performance. RESULTS: On average, patients performed worse than controls at a large effect size. When the total score of the Impact on Performance Scale was accounted for by mediation analysis and analyses of covariance, group differences were reduced to a medium effect size. A total of 30% of patients showed impairments of at least one standard deviation below the mean. CONCLUSIONS: This study confirms that neurocognitive impairment in depression is likely overestimated; future studies should consider fair test-taking conditions. We advise researchers to report percentages of patients showing performance deficits rather than relying solely on overall group differences. This prevents fostering the impression that the majority of patients exert deficits, when in fact deficits are only true for a subgroup.
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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 | 0.065 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".